Learning and transfer of category knowledge in an indirect categorization task

Hélie, Sébastien; Ashby, F. Gregory

doi:10.1007/s00426-011-0348-1

Cited by 26 publications

(17 citation statements)

References 20 publications

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“…Walking is also usually done automatically. One essential skill required to function in everyday life is the ability to recognize and categorize objects (Helie & Ashby, 2012). For example, people need to be able to categorize faces and scenes accurately without expending too much effort in order to determine appropriate sets of actions.…”

Section: Introductionmentioning

confidence: 99%

Changes in default mode network as automaticity develops in a categorization task

Shamloo

Hélie

2016

Behavioural Brain Research

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The default mode network (DMN) is a set of brain regions in which blood oxygen level dependent signal is suppressed during attentional focus on the external environment. Because automatic task processing requires less attention, development of automaticity in a rule-based categorization task may result in less deactivation and altered functional connectivity of the DMN when compared to the initial learning stage. We tested this hypothesis by re-analyzing functional magnetic resonance imaging data of participants trained in rule-based categorization for over 10,000 trials (Helie et al., 2010) [12,13]. The results show that some DMN regions are deactivated in initial training but not after automaticity has developed. There is also a significant decrease in DMN deactivation after extensive practice. Seed-based functional connectivity analyses with the precuneus, medial prefrontal cortex (two important DMN regions) and Brodmann area 6 (an important region in automatic categorization) were also performed. The results show increased functional connectivity with both DMN and non-DMN regions after the development of automaticity, and a decrease in functional connectivity between the medial prefrontal cortex and ventromedial orbitofrontal cortex. Together, these results further support the hypothesis of a strategy shift in automatic categorization and bridge the cognitive and neuroscientific conceptions of automaticity in showing that the reduced need for cognitive resources in automatic processing is accompanied by a disinhibition of the DMN and stronger functional connectivity between DMN and task-related brain regions.

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Section: Introductionmentioning

confidence: 99%

Changes in default mode network as automaticity develops in a categorization task

Shamloo

Hélie

2016

Behavioural Brain Research

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“…For example, rule-use allows for a direct generalization of performance to new stimuli or new situations (Helie & Ashby, 2012). As a result, rule learning is essential for simple everyday tasks such as classifying new animals as friends or foes, or learning the meaning of traffic signs.…”

Section: Introductionmentioning

confidence: 99%

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

Hélie¹,

Ell

Filoteo

et al. 2015

Brain and Cognition

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In perceptual categorization, rule selection consists of selecting one or several stimulus-dimensions to be used to categorize the stimuli (e.g, categorize lines according to their length). Once a rule has been selected, criterion learning consists of defining how stimuli will be grouped using the selected dimension(s) (e.g., if the selected rule is line length, define ‘long’ and ‘short’). Very little is known about the neuroscience of criterion learning, and most existing computational models do not provide a biological mechanism for this process. In this article, we introduce a new model of rule learning called Heterosynaptic Inhibitory Criterion Learning (HICL). HICL includes a biologically-based explanation of criterion learning, and we use new category-learning data to test key aspects of the model. In HICL, rule selective cells in prefrontal cortex modulate stimulus-response associations using pre-synaptic inhibition. Criterion learning is implemented by a new type of heterosynaptic error-driven Hebbian learning at inhibitory synapses that uses feedback to drive cell activation above/below thresholds representing ionic gating mechanisms. The model is used to account for new human categorization data from two experiments showing that: (1) changing rule criterion on a given dimension is easier if irrelevant dimensions are also changing (Experiment 1), and (2) showing that changing the relevant rule dimension and learning a new criterion is more difficult, but also facilitated by a change in the irrelevant dimension (Experiment 2). We conclude with a discussion of some of HICL’s implications for future research on rule learning.

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“…Classification and concept training are popular training methods in the category learning literature [ 2 , 6 , 14 – 17 , 27 , 40 ]. Classification training instructions emphasize learning to distinguish between members of different categories (A/B training; e.g., Is the stimulus a member of category A or category B?).…”

Section: Discussionmentioning

confidence: 99%

“…Categorical representations (i.e., the way in which information is stored and used [ 1 ]) are the building blocks of decision–making from the most routine to the most novel contexts [ 2 ]. Not surprisingly, the study of the processes underlying the development of the representations necessary for such decision making has been the focus of much research [ 3 – 6 ].…”

Section: Introductionmentioning

confidence: 99%

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The effect of training methodology on knowledge representation in categorization

2017

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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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Learning and transfer of category knowledge in an indirect categorization task

Cited by 26 publications

References 20 publications

Changes in default mode network as automaticity develops in a categorization task

Changes in default mode network as automaticity develops in a categorization task

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

The effect of training methodology on knowledge representation in categorization

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