scholarly journals Learning exceptions to the rule in human and model via hippocampal encoding

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Emily M. Heffernan ◽  
Margaret L. Schlichting ◽  
Michael L. Mack
Keyword(s):  
Category Learning ◽  
Computational Modelling ◽  
General Rule ◽  
Memory Formation ◽  
Learning Systems ◽  
Similarity Analysis ◽  
Hippocampal Function ◽  
Learning Sequence ◽  
Common Category ◽  
Modelling Data

AbstractCategory learning helps us process the influx of information we experience daily. A common category structure is “rule-plus-exceptions,” in which most items follow a general rule, but exceptions violate this rule. People are worse at learning to categorize exceptions than rule-following items, but improved exception categorization has been positively associated with hippocampal function. In light of model-based predictions that the nature of existing memories of related experiences impacts memory formation, here we use behavioural and computational modelling data to explore how learning sequence impacts performance in rule-plus-exception categorization. Our behavioural results indicate that exception categorization accuracy improves when exceptions are introduced later in learning, after exposure to rule-followers. To explore whether hippocampal learning systems also benefit from this manipulation, we simulate our task using a computational model of hippocampus. The model successful replicates our behavioural findings related to exception learning, and representational similarity analysis of the model’s hidden layers suggests that model representations are impacted by trial sequence: delaying the introduction of an exception shifts its representation closer to its own category members. Our results provide novel computational evidence of how hippocampal learning systems can be targeted by learning sequence and bolster extant evidence of hippocampus’s role in category learning.

scholarly journals Learning exceptions to the rule in human and model via hippocampal encoding

2021 ◽  
Author(s):  
Emily Heffernan ◽  
Margaret Schlichting ◽  
Michael Louis Mack
Keyword(s):  
Category Learning ◽  
Computational Modelling ◽  
General Rule ◽  
Memory Formation ◽  
Hippocampal Function ◽  
Learning Sequence ◽  
Rule Following ◽  
Hippocampal Subfields ◽  
The Impact ◽  
Modelling Data

Category learning helps us process the influx of information we experience daily. A commonly encountered category structure is “rule-plus-exceptions,” in which most items follow a general rule, but exceptions violate this rule. People are worse at learning to categorize exceptions than rule-following items, but improved exception categorization has been positively associated with hippocampal function. In light of model-based predictions that the nature of existing memories of related experiences should impact memory formation, here we use behavioural and computational modelling data to explore the impact of learning sequence on performance in a rule-plus-exception categorization task. Our behavioural results indicate that exception categorization accuracy improves when exceptions are introduced later in learning, after exposure to rule-following stimuli. Simulations of this task using a computational model of hippocampus replicate these behavioural findings. Representational similarity analysis of the model’s hidden layers, which correspond to hippocampal subfields, suggests that model representations are impacted by trial sequence: delaying the introduction of an exception shifts its representation closer to those of its own category members; this finding corroborates the superior categorization behaviour observed for delayed exceptions. Our results provide novel computational evidence of HC’s sensitivity to learning sequence and further support HC’s proposed role in category learning.

scholarly journals Cognitive Changes in Conjunctive Rule-Based Category Learning: An ERP Approach

2017 ◽  
Author(s):  
Rahel Rabi ◽  
Marc F Joanisse ◽  
Tianshu Zhu ◽  
John Paul Minda
Keyword(s):  
Working Memory ◽  
Category Learning ◽  
Computational Modelling ◽  
Learning Rule ◽  
Event Related Potentials ◽  
Cognitive Changes ◽  
Rule Based ◽  
Differential Processing ◽  
Conjunctive Rule ◽  
Related Potentials

PreprintWhen learning rule-based categories, sufficient cognitive resources are needed to test hypotheses, maintain the currently active rule in working memory, update rules after feedback, and to select a new rule if necessary. Prior research has demonstrated that conjunctive rules are more complex than unidimensional rules and place greater demands on executive functions like working memory. In our study, event-related potentials (ERPs) were recorded while participants performed a conjunctive rule-based category learning task with trial-by-trial feedback. In line with prior research, correct categorization responses resulted in a larger stimulus-locked late positive complex compared to incorrect responses, possibly indexing the updating of rule information in memory. Incorrect trials elicited a pronounced feedback-locked P300 elicited which suggested a disconnect between perception, and the rule-based strategy. We also examined the differential processing of stimuli that were able to be correctly classified by the suboptimal single-dimensional rule (“easy” stimuli) versus those that could only be correctly classified by the optimal, conjunctive rule (“difficult” stimuli). Among strong learners, a larger, late positive slow wave emerged for difficult compared to easy stimuli, suggesting differential processing of category items even though strong learners performed well on the conjunctive category set. Overall, the findings suggest that ERP combined with computational modelling can be used to better understand the cognitive processes involved in rule-based category learning

scholarly journals Tests of a dual-system model of speech category learning

2014 ◽  
Vol 17 (4) ◽  
pp. 709-728 ◽  
Author(s):  
W. TODD MADDOX ◽  
BHARATH CHANDRASEKARAN
Keyword(s):  
Category Learning ◽  
Native Speakers ◽  
American English ◽  
Dual System ◽  
Learning Systems ◽  
Practice Transfer ◽  
Mandarin Tones ◽  
Visual Domain ◽  
Speech Learning

In the visual domain, more than two decades of work has argued for the existence of dual category learning systems. Thereflectivesystem uses working memory in an explicit fashion to develop and test rules for classifying. Thereflexivesystem operates by implicitly associating perception with actions that lead to reinforcement. Dual-system models posit that in learning natural categories, learners initially use the reflective system and with practice, transfer control to the reflexive system. The role of reflective and reflexive systems in second language (L2) speech learning has not been systematically examined. In the study reported in this paper, monolingual native speakers of American English were trained to categorize Mandarin tones produced by multiple speakers. Our computational modeling approach demonstrates that learners use reflective and reflexive strategies during tone category learning. Successful learners use speaker-dependent, reflective analysis early in training and reflexive strategies by the end of training. Our results demonstrate that dual-learning systems are operative in L2 speech learning. Critically, learner strategies directly relate to individual differences in successful category learning.

scholarly journals Toward a dual-learning systems model of speech category learning

2014 ◽  
Vol 5 ◽  
Author(s):  
Bharath Chandrasekaran ◽  
Seth R. Koslov ◽  
W. T. Maddox
Keyword(s):  
Category Learning ◽  
Learning Systems ◽  
Systems Model

scholarly journals Hierarchical control of procedural and declarative category-learning systems

NeuroImage ◽  
2017 ◽  
Vol 150 ◽  
pp. 150-161 ◽  
Author(s):  
Benjamin O. Turner ◽  
Matthew J. Crossley ◽  
F. Gregory Ashby
Keyword(s):  
Category Learning ◽  
Hierarchical Control ◽  
Learning Systems

scholarly journals Post-encoding reactivation promotes one-shot learning of episodes in humans

2021 ◽  
Author(s):  
Xiongbo Wu ◽  
Xavier Viñals ◽  
Aya Ben-Yakov ◽  
Bernhard P. Staresina ◽  
Lluís Fuentemilla
Keyword(s):  
Memory Formation ◽  
Learning Processes ◽  
Similarity Analysis ◽  
Male And Female ◽  
Memory Reactivation ◽  
Representational Similarity Analysis ◽  
Initial Learning ◽  
Human Participants ◽  
Meaningful Event

AbstractMuch work in rodents and in humans has provided evidence that post-encoding reinstatement plays an important role in stabilizing memory beyond initial learning processes. However, it remains unclear whether memory reinstatement is important for the rapid - ‘one-shot’ - learning of an unfolding episode. Here, we asked whether the reinstatement of an episode may occur preferentially post-encoding, when an individual perceives a meaningful event to be concluded. We asked human participants (male and female) to encode sequences of pictures depicting unique episodic-like events. We used representational similarity analysis of scalp electroencephalography recordings during encoding and found evidence for memory reactivation of the just encoded sequence of elements at the offset of the episode. Importantly, memory reinstatement was not observed between successive elements within an episode, indicating memory reactivation was specifically induced once participants perceived the unfolding episode to be completed. We also found that memory reinstatement predicted memory recollection of an encoded episode and that offset memory reinstatement was not present when participants encoded sequences of pictures that were not perceived as meaningful episodes. These results indicate that memory reinstatement at episode offsets is a mechanism selectively engaged to support rapid memory formation of single events.

Sign in / Sign up

Export Citation Format

Share Document