The Neural Correlates of Similarity- and Rule-based Generalization

Rule and Exemplar-based Transfer in Category Learning

We compared the neural systems involved in transfer to novel stimuli via rule application versus exemplar processing. Participants learned a categorization task involving abstraction of a complex rule and then categorized different types of transfer stimuli without feedback. Rule stimuli used new features and therefore could only be categorized using the rule. Exemplar stimuli included only one of the features necessary to apply the rule and therefore required participants to categorize based on similarity to individual previously learned category members. Consistent and inconsistent stimuli were formed so that both the rule and feature similarity indicated the same category (consistent) or opposite categories (inconsistent). We found that all conditions eliciting rule-based transfer recruited a medial prefrontal-anterior hippocampal network associated with schematic memory. In contrast, exemplar-based transfer recruited areas of the intraparietal sulcus associated with learning and executing stimulus-category mappings along with the posterior hippocampus. These results support theories of categorization that postulate complementary learning and generalization strategies based on schematic and exemplar mechanisms.

Multisensory Information Facilitates the Categorization of Untrained Stimuli

Although it has been demonstrated that multisensory information can facilitate object recognition and object memory, it remains unclear whether such facilitation effect exists in category learning. To address this issue, comparable car images and sounds were first selected by a discrimination task in Experiment 1. Then, those selected images and sounds were utilized in a prototype category learning task in Experiments 2 and 3, in which participants were trained with auditory, visual, and audiovisual stimuli, and were tested with trained or untrained stimuli within the same categories presented alone or accompanied with a congruent or incongruent stimulus in the other modality. In Experiment 2, when low-distortion stimuli (more similar to the prototypes) were trained, there was higher accuracy for audiovisual trials than visual trials, but no significant difference between audiovisual and auditory trials. During testing, accuracy was significantly higher for congruent trials than unisensory or incongruent trials, and the congruency effect was larger for untrained high-distortion stimuli than trained low-distortion stimuli. In Experiment 3, when high-distortion stimuli (less similar to the prototypes) were trained, there was higher accuracy for audiovisual trials than visual or auditory trials, and the congruency effect was larger for trained high-distortion stimuli than untrained low-distortion stimuli during testing. These findings demonstrated that higher degree of stimuli distortion resulted in more robust multisensory effect, and the categorization of not only trained but also untrained stimuli in one modality could be influenced by an accompanying stimulus in the other modality.

The role of working memory and visual processing in prototype category learning

To investigate whetherworking memory and visual processing havethe same role or differentrolesin A/B and A/not A prototype category learning,the present study adoptedan A/Bor A/not A category learningtask in control and dual conditions. The results of Experiment 1 showed that an additional dual visual working memory taskrather thanadualverbal working memory task reduced accuracy of the A/B task, whereasnodual tasksinfluencedaccuracy of the A/not A task. The results of Experiment 2 revealed that an additionaldual visual processing task impairedaccuracy of the A/B task, whereas the dual visual processing task did not influence accuracy of the A/not Atask. These results indicate that visual working memory and visual processing play different roles in A/B and A/not A prototype category learning, andsupport that thesetwo types of prototype category learning are mediated by differentmemory systems.

The impact of training methodology and representation on rule-based categorization: An fMRI study

Hélie, Shamloo, & Ell (2017) showed that regular classification learning instructions (A/B) promote between-category knowledge in rule-based categorization whereas conceptual learning instructions (YES/NO) promote learning within-category knowledge with the same categories. Here we explore how these tasks affect brain activity using fMRI. Participants learned two sets of two categories. Computational models were fit to the behavioral data to determine the type of knowledge learned by each participant. fMRI contrasts were computed to compare BOLD signal between the tasks and between the types of knowledge. The results show that participants in the YES/NO task had more activity in the pre-supplementary motor area, prefrontal cortex, and the angular/supramarginal gyrus. These brain areas are related to working memory and part of the dorsal attention network, which showed increased task-based functional connectivity with the medial temporal lobes. In contrast, participants in the A/B task had more activity in the thalamus and caudate. These results suggest that participants in the YES/NO task used bivalent rules and may have treated each contextual question as a separate task, switching task each time the question changed. Activity in the A/B condition was more consistent with participants applying direct Stimulus → Response rules. With regards to knowledge representation, there was a large shared network of brain areas, but participants learning between-category information showed additional posterior parietal activity, which may be related to the inhibition of incorrect motor programs.

Stimulus modality influences the acquisition and use of the rule-based strategy and the similarity-based strategy in category learning

This study aimed to investigate whether stimulus modality influenced the acquisition and use of the rule-based strategy and the similarity-based strategy in category learning and whether the use of the two strategies was supported by shared or separate neural substrates. To address these issues, we combined behavioral and fNIRS methods in a modified prototype distortion task in which each category member has one rule feature and ten similarity features, and each type of feature can be presented in either the visual modality or the auditory modality. The results in Experiment 1 revealed that the learning effect in the "auditory rule-visual similarity" condition was the highest among all four conditions; further analysis revealed that in the "auditory rule-visual similarity" condition, the number of participants who used the rule-based strategy was more than the number of participants who used the similarity-based strategy, and the learning effect was always much higher for the rule-based strategy than for the similarity-based strategy. The behavioral results in Experiment 2 replicated the main findings in Experiment 1, and the fNIRS results showed that the use of the visual rule-based strategy was mediated by the dorsolateral prefrontal cortex, whereas the use of the auditory similarity-based strategy mainly engaged in the superior temporal gyrus, and the use of the visual similarity-based strategy mainly engaged in the inferior temporal gyrus. The results in Experiment 3 revealed that when the stimuli had only one type of feature, the visual rule rather than the auditory rule was learned more easily. The results provide new evidence that the stimulus modality can influence the acquisition and use of the rule-based strategy and the similarity-based strategy in category learning and that the use of the two types of strategies is supported by separate neural substrates both in the auditory modality and the visual modality.

Dissociable learning processes, associative theory, and testimonial reviews: A comment on Smith and Church (2018)

Smith and Church (Psychonomic Bulletin & Review, 25, 1565-1584 2018) present a "testimonial" review of dissociable learning processes in comparative and cognitive psychology, by which we mean they include only the portion of the available evidence that is consistent with their conclusions. For example, they conclude that learning the information-integration category-learning task with immediate feedback is implicit, but do not consider the evidence that people readily report explicit strategies in this task, nor that this task can be accommodated by accounts that make no distinction between implicit and explicit processes. They also consider some of the neuroscience relating to information-integration category learning, but do not report those aspects that are more consistent with an explicit than an implicit account. They further conclude that delay conditioning in humans is implicit, but do not report evidence that delay conditioning requires awareness; nor do they present the evidence that conditioned taste aversion, which should be explicit under their account, can be implicit. We agree with Smith and Church that it is helpful to have a clear definition of associative theory, but suggest that their definition may be unnecessarily restrictive. We propose an alternative definition of associative theory and briefly describe an experimental procedure that we think may better distinguish between associative and non-associative processes.

Learning strategies and the social brain: Missing elements in the link between developmental stress, song and cognition?

Bird songs may advertise aspects of cognition because song learning and learning speed in cognitive tasks are both affected by early-life environments. However, such relationships remain ambiguous in the literature. Here, I discuss 2 lines of research that may help to demystify links between song learning and cognition. First, learning strategies should be considered when assessing performance to ensure that individual differences in learning ability are not masked by individual differences in learning strategies. Second, song characteristics should be associated with social behavior because songs have a social purpose and, consequently, should be strongly related at functional and neural levels. Finally, if song learning and cognitive abilities are correlated because they develop concurrently and/or share or compete for the same resources, I discuss ways glucocorticoids may link early-life stress, song learning and cognitive ability, focusing particularly on oxidative stress as a potential mechanism.

A Similarity-Based Process for Human Judgment in the Parietal Cortex

One important distinction in psychology is between inferences based on associative memory and inferences based on analysis and rules. Much previous empirical work conceive of associative and analytical processes as two exclusive ways of addressing a judgment task, where only one process is selected and engaged at a time, in an either-or fashion. However, related work indicate that the processes are better understood as being in interplay and simultaneously engaged. Based on computational modeling and brain imaging of spontaneously adopted judgment strategies together with analyses of brain activity elicited in tasks where participants were explicitly instructed to perform similarity-based associative judgments or rule-based judgments (n = 74), we identified brain regions related to the two types of processes. We observed considerable overlap in activity patterns. The precuneus was activated for both types of judgments, and its activity predicted how well a similarity-based model fit the judgments. Activity in the superior frontal gyrus predicted the fit of a rule-based judgment model. The results suggest the precuneus as a key node for similarity-based judgments, engaged both when overt responses are guided by similarity-based and rule-based processes. These results are interpreted such that similarity-based processes are engaged in parallel to rule-based-processes, a finding with direct implications for cognitive theories of judgment.

A Meta-Analysis Suggests Different Neural Correlates for Implicit and Explicit Learning

A meta-analysis of non-human primates performing three different tasks (Object-Match, Category-Match, and Category-Saccade associations) revealed signatures of explicit and implicit learning. Performance improved equally following correct and error trials in the Match (explicit) tasks, but it improved more after correct trials in the Saccade (implicit) task, a signature of explicit versus implicit learning. Likewise, error-related negativity, a marker for error processing, was greater in the Match (explicit) tasks. All tasks showed an increase in alpha/beta (10-30 Hz) synchrony after correct choices. However, only the implicit task showed an increase in theta (3-7 Hz) synchrony after correct choices that decreased with learning. In contrast, in the explicit tasks, alpha/beta synchrony increased with learning and decreased thereafter. Our results suggest that explicit versus implicit learning engages different neural mechanisms that rely on different patterns of oscillatory synchrony.

Individual Difference Factors in the Learning and Transfer of Patterning Discriminations

In an associative patterning task, some people seem to focus more on learning an overarching rule, whereas others seem to focus on acquiring specific relations between the stimuli and outcomes involved. Building on earlier work, we further investigated which cognitive factors are involved in feature- vs. rule-based learning and generalization. To this end, we measured participants' tendency to generalize according to the rule of opposites after training on negative and positive patterning problems (i.e., A+/B+/AB- and C-/D-/CD+), their tendency to attend to global aspects or local details of stimuli, their systemizing disposition and their score on the Raven intelligence test. Our results suggest that while intelligence might have some influence on patterning learning and generalization, visual processing style and systemizing disposition do not. We discuss our findings in the light of previous observations on patterning.