Age differences in functional connectivity track dedifferentiation of category representations.

With advancing age, the distinctiveness of neural representations of information declines. While the finding of this so-called 'age-related neural dedifferentiation' in category-selective neural regions is well-described, the contribution of age-related changes in network organization to dedifferentiation is unknown. Here, we asked whether age differences in a) whole-brain network segregation (i.e., network dedifferentiation) and b) functional connectivity to category-selective neural regions contribute to regional dedifferentiation of categorical representations. Younger and older adults viewed blocks of face and house stimuli in the fMRI scanner. We found an age-related decline in neural distinctiveness for faces in the fusiform gyrus (FG) and for houses in the parahippocampal gyrus (PHG). Functional connectivity analyses revealed age-related dedifferentiation of global network structure as well as age differences in connectivity between the FG and early visual cortices. Interindividual correlations demonstrated that regional distinctiveness was related to network segregation as well as connectivity of the FG to the visual network. Together, our findings reveal that dedifferentiation of categorical representations may be linked to age-related reorganization of functional networks.

Coherent category training enhances generalization in prototype-based categories.

A major question for the study of learning and memory is how to tailor learning experiences to promote knowledge that generalizes to new situations. In two experiments, we used category learning as a representative domain to test two factors thought to influence the acquisition of conceptual knowledge: the number of training examples (set size) and the similarity of training examples to the category average (set coherence). Across participants, size and coherence of category training sets were varied in a fully crossed design. After training, participants demonstrated the breadth of their category knowledge by categorizing novel examples varying in their distance from the category center. Results showed better generalization following more coherent training sets, even when categorizing items furthest from the category center. Training set size had limited effects on performance. We also tested the types of representations underlying categorization decisions by fitting formal prototype and exemplar models. Prototype models posit abstract category representations based on the category's central tendency, whereas exemplar models posit that categories are represented by individual category members. In Experiment 1, low coherence training led to fewer participants relying on prototype representations, except when training length was extended. In Experiment 2, low coherence training led to chance performance and no clear representational strategy for nearly half of the participants. The results indicate that highlighting commonalities among exemplars during training facilitates learning and generalization and may also affect the types of concept representations that individuals form. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Evaluating methods for measuring background connectivity in slow event-related functional magnetic resonance imaging designs.

INTRODUCTION: Resting-state functional magnetic resonance imaging (fMRI) is widely used for measuring functional interactions between brain regions, significantly contributing to our understanding of large-scale brain networks and brain-behavior relationships. Furthermore, idiosyncratic patterns of resting-state connections can be leveraged to identify individuals and predict individual differences in clinical symptoms, cognitive abilities, and other individual factors. Idiosyncratic connectivity patterns are thought to persist across task states, suggesting task-based fMRI can be similarly leveraged for individual differences analyses. METHOD: Here, we tested the degree to which functional interactions occurring in the background of a task during slow event-related fMRI parallel or differ from those captured during resting-state fMRI. We compared two approaches for removing task-evoked activity from task-based fMRI: (1) applying a low-pass filter to remove task-related frequencies in the signal, or (2) extracting residuals from a general linear model (GLM) that accounts for task-evoked responses. RESULT: We found that the organization of large-scale cortical networks and individual's idiosyncratic connectivity patterns are preserved during task-based fMRI. In contrast, individual differences in connection strength can vary more substantially between rest and task. Compared to low-pass filtering, background connectivity obtained from GLM residuals produced idiosyncratic connectivity patterns and individual differences in connection strength that more resembled rest. However, all background connectivity measures were highly similar when derived from the low-pass-filtered signal or GLM residuals, indicating that both methods are suitable for measuring background connectivity. CONCLUSION: Together, our results highlight new avenues for the analysis of task-based fMRI datasets and the utility of each background connectivity method.

Generalization and false memory in acquired equivalence.

Memory allows us to remember specific events but also combine information across events to infer new information. New inferences are thought to stem from integrating memories of related events during encoding but can be also generated on-demand, based on separate memories of individual events. Integrative encoding has been argued as dominant in the acquired equivalence paradigm, where people have a tendency to assume that when two faces share one preference, they also share another. A downside may be a loss of source memory, where inferred preferences are mistaken for observed ones. Here, we tested these predictions of the integrative encoding hypothesis across five datasets collected using variations of the acquired equivalence paradigm. Results showed a statistically reliable but numerically small tendency to generalize preferences across faces, with stronger evidence for on-demand inferences at retrieval rather than spontaneous integration during encoding. A newly included explicit source memory test showed that participants differentiated learned from inferred preferences to a high degree, irrespective of whether they generalized preferences across faces. False memory was however increased in participants who made generalization decisions faster, which could be consistent with integrative encoding and/or source monitoring frameworks. The results suggest that generalization in acquired equivalence may result from integrated representations that facilitate new inferences at the expense of source memory, but also demonstrate that on-demand retrieval-based processes may play a larger role in this paradigm than previously thought. Finally, the results indicate that reaction times may be more sensitive than performance as a means to assess representations underlying behavior. More broadly, the study informs current theories of generalization and knowledge representation and provides new insights into how memory biases decisions.

The effects of age on category learning and prototype- and exemplar-based generalization.

The need to learn new concepts and categories persists through the lifespan, yet little is known about how aging affects the concept learning and generalization. Here, we trained young and older adults to classify typical and boundary category members, and then tested category generalization to new stimuli. During training, older adults had increased difficulty compared to young adults learning category labels for boundary items, but not typical items. At test, categorization performance that included new items at all levels of typicality was comparable across age groups, but formal categorization models indicated that older adults relied to a greater degree on generalized (prototype) category representations than young adults. These findings align with the proposal that older adults are able to form category representations based on central tendency even when they have difficulty learning and remembering individual category members. More broadly, the results contribute to our understanding of multiple categorization strategies and the limited strategy flexibility in older adults. They also highlight how reliance on preserved cognitive functions may sometimes help older adults maintain performance. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Age effects on category learning, categorical perception, and generalization.

Age deficits in memory for individual episodes are well established. Less is known about how age affects another key memory function: the ability to form new conceptual knowledge. Here we studied age differences in concept formation in a category-learning paradigm with face-blend stimuli, using several metrics: direct learning of category members presented during training, generalisation of category labels to new examples, and shifts in perceived similarity between category members that often follow category learning. We found that older adults were impaired in direct learning of training examples, but that there was no significant age deficit in generalisation once we accounted for the deficit in direct learning. We also found that category learning affected the perceived similarity between members of the same versus opposing categories, and age did not significantly moderate this effect. Lastly, we compared traditional category learning to categorisation after a learning task in which a category label (shared last name) was presented alongside stimulus-specific information (unique first names that individuated category members). We found that simultaneously learning stimulus-specific and category information resulted in decreased category learning, and that this decrement was apparent in both age groups.

Category-Biased Neural Representations Form Spontaneously during Learning That Emphasizes Memory for Specific Instances.

Category learning, learning to sort a set of stimuli into categories or groups, can induce category biases in perception such that items in the same category are perceived as more similar than items from different categories. To what degree category bias develops when learning goals emphasize individuation of each stimulus and whether the bias emerges spontaneously during learning itself rather than in response to task demands is unclear. Here, we used functional MRI (fMRI) during encoding to test for category biases in neural representations of individual stimuli during learning. Human participants (males and females) encountered face-blend stimuli with unique first names and shared family names that indicated category membership. Participants were instructed to learn the full name for each face. Neural pattern classification and pattern similarity analyses were used to track category information in the brain. Results showed that stimulus category could be decoded during encoding across many frontal, parietal, and occipital regions. Furthermore, two stimuli from the same category were represented more similarly in the prefrontal cortex than two stimuli from different categories equated for physical similarity. These findings illustrate that a mere presence of category label can bias neural representations spontaneously during encoding to emphasize category-relevant information, even in the absence of explicit categorization demands and when category-irrelevant information remains relevant for task goals.SIGNIFICANCE STATEMENT Entities belonging to the same category are perceived as being more similar than entities belonging to different categories. Here, we show that neural representations highlighting category-relevant information form spontaneously during encoding. Notably, the presence of a category label led to neural category bias although participants focused on remembering individual stimuli and category-irrelevant stimulus features remained important for explicit task goals. These results may inform our understanding of bias in general and suggest that bias may emerge when category information is present even when one's explicit focus is on individuals.

Generalization and False Memory in an Acquired Equivalence Paradigm: The Influence of Physical Resemblance Across Related Episodes.

The ability to make inferences about related experiences is an important function of memory that allows individuals to build generalizable knowledge. In some cases, however, making inferences may lead to false memories when individuals misremember inferred information as having been observed. One factor that is known to increase the prevalence of false memories is the physical resemblance between new and old information. The extent to which physical resemblance has parallel effects on generalization and memory for the source of inferred associations is not known. To investigate the parallels between memory generalization and false memories, we conducted three experiments using an acquired equivalence paradigm and manipulated physical resemblance between items that made up related experiences. The three experiments showed increased generalization for higher levels of resemblance. Recognition and source memory judgments revealed that high rates of generalization were not always accompanied by high rates of false memories. Thus, physical resemblance across episodes may promote generalization with or without a trade-off in terms of impeding memory specificity.

Characterizing the impact of adversity, abuse, and neglect on adolescent amygdala resting-state functional connectivity.

Characterizing typologies of childhood adversity may inform the development of risk profiles and corresponding interventions aimed at mitigating its lifelong consequences. A neurobiological grounding of these typologies requires systematic comparisons of neural structure and function among individuals with different exposure histories. Using seed-to-whole brain analyses, this study examined associations between childhood adversity and amygdala resting-state functional connectivity (rs-fc) in adolescents aged 11-19 years across three independent studies (N = 223; 127 adversity group) in both general and dimensional models of adversity (comparing abuse and neglect). In a general model, adversity was associated with altered amygdala rs-fc with clusters within the left anterior lateral prefrontal cortex. In a dimensional model, abuse was associated with altered amygdala rs-fc within the orbitofrontal cortex, dorsal precuneus, posterior cingulate cortex, and dorsal anterior cingulate cortex/anterior mid-cingulate cortex, as well as within the dorsal attention, visual, and somatomotor networks. Neglect was associated with altered amygdala rs-fc with the hippocampus, supplementary motor cortex, temporoparietal junction, and regions within the dorsal attention network. Both general and dimensional models revealed unique regions, potentially reflecting pathways by which distinct histories of adversity may influence adolescent behavior, cognition, and psychopathology.

How do we generalize?

Humans and animals are able to generalize or transfer information from previous experience so that they can behave appropriately in novel situations. What mechanisms-computations, representations, and neural systems-give rise to this remarkable ability? The members of this Generative Adversarial Collaboration (GAC) come from a range of academic backgrounds but are all interested in uncovering the mechanisms of generalization. We started out this GAC with the aim of arbitrating between two alternative conceptual accounts: (1) generalization stems from integration of multiple experiences into summary representations that reflect generalized knowledge, and (2) generalization is computed on-the-fly using separately stored individual memories. Across the course of this collaboration, we found that-despite using different terminology and techniques, and although some of our specific papers may provide evidence one way or the other-we in fact largely agree that both of these broad accounts (as well as several others) are likely valid. We believe that future research and theoretical synthesis across multiple lines of research is necessary to help determine the degree to which different candidate generalization mechanisms may operate simultaneously, operate on different scales, or be employed under distinct conditions. Here, as the first step, we introduce some of these candidate mechanisms and we discuss the issues currently hindering better synthesis of generalization research. Finally, we introduce some of our own research questions that have arisen over the course of this GAC, that we believe would benefit from future collaborative efforts.

Tracking prototype and exemplar representations in the brain across learning.

There is a long-standing debate about whether categories are represented by individual category members (exemplars) or by the central tendency abstracted from individual members (prototypes). Neuroimaging studies have shown neural evidence for either exemplar representations or prototype representations, but not both. Presently, we asked whether it is possible for multiple types of category representations to exist within a single task. We designed a categorization task to promote both exemplar and prototype representations and tracked their formation across learning. We found only prototype correlates during the final test. However, interim tests interspersed throughout learning showed prototype and exemplar representations across distinct brain regions that aligned with previous studies: prototypes in ventromedial prefrontal cortex and anterior hippocampus and exemplars in inferior frontal gyrus and lateral parietal cortex. These findings indicate that, under the right circumstances, individuals may form representations at multiple levels of specificity, potentially facilitating a broad range of future decisions.

Generalization and the hippocampus: More than one story?

Memory-based cognition depends on both the ability to remember specific details of individual experiences and the ability to combine information across experiences to generalize and derive new knowledge. A hippocampal role in rapid encoding of specific events is long established. More recent research also demonstrates hippocampal contributions to generalization, but their nature is still debated. The current review provides an overview of hippocampal-based generalization in two lines of research-episodic inference and categorization-and discusses evidence for four candidate mechanisms and representational schemes that may underpin such generalization. We highlight evidence showing that the hippocampus contributes specific memories to generalization decisions, but also forms generalized representations that integrate information across experiences. Multiple views are currently plausible of how such generalized representations form and relate to specific memories. Future research that uses behavioral and neural indices of both generalization and specificity may help resolve between the candidate generalization mechanisms, with the possibility that more than one view of hippocampal-based generalization may be valid. Importantly, all views share the emphasis on the broader role of the hippocampus in cognition that goes beyond remembering the past.

Perceived similarity ratings predict generalization success after traditional category learning and a new paired-associate learning task.

The current study investigated category learning across two experiments using face-blend stimuli that formed face families controlled for within- and between-category similarity. Experiment 1 was a traditional feedback-based category-learning task, with three family names serving as category labels. In Experiment 2, the shared family name was encountered in the context of a face-full name paired-associate learning task, with a unique first name for each face. A subsequent test that required participants to categorize new faces from each family showed successful generalization in both experiments. Furthermore, perceived similarity ratings for pairs of faces were collected before and after learning, prior to generalization test. In Experiment 1, similarity ratings increased for faces within a family and decreased for faces that were physically similar but belonged to different families. In Experiment 2, overall similarity ratings decreased after learning, driven primarily by decreases for physically similar faces from different families. The post-learning category bias in similarity ratings was predictive of subsequent generalization success in both experiments. The results indicate that individuals formed generalizable category knowledge prior to an explicit demand to generalize and did so both when attention was directed towards category-relevant features (Experiment 1) and when attention was directed towards individuating faces within a family (Experiment 2). The results tie together research on category learning and categorical perception and extend them beyond a traditional category-learning task.

Spatiotemporal Dynamics of Multiple Memory Systems During Category Learning.

The brain utilizes distinct neural mechanisms that ease the transition through different stages of learning. Furthermore, evidence from category learning has shown that dissociable memory systems are engaged, depending on the structure of a task. This can even hold true for tasks that are very similar to each other, which complicates the process of classifying brain activity as relating to changes that are associated with learning or reflecting the engagement of a memory system suited for the task. The primary goals of these studies were to characterize the mechanisms that are associated with category learning and understand the extent to which different memory systems are recruited within a single task. Two studies providing spatial and temporal distinctions between learning-related changes in the brain and category-dependent memory systems are presented. The results from these experiments support the notion that exemplar memorization, rule-based, and perceptual similarity-based categorization are flexibly recruited in order to optimize performance during a single task. We conclude that these three methods, along with the memory systems they rely on, aid in the development of expertise, but their engagement might depend on the level of familiarity with a category.

Training set coherence and set size effects on concept generalization and recognition.

Building conceptual knowledge that generalizes to novel situations is a key function of human memory. Category-learning paradigms have long been used to understand the mechanisms of knowledge generalization. In the present study, we tested the conditions that promote formation of new concepts. Participants underwent 1 of 6 training conditions that differed in the number of examples per category (set size) and their relative similarity to the category average (set coherence). Performance metrics included rates of category learning, ability to generalize categories to new items of varying similarity to prototypes, and recognition memory for individual examples. In categorization, high set coherence led to faster learning and better generalization, while set size had little effect. Recognition did not differ reliably among conditions. We also tested the nature of memory representations used for categorization and recognition decisions using quantitative prototype and exemplar models fit to behavioral responses. Prototype models posit abstract category representations based on the category's central tendency, whereas exemplar models posit that categories are represented by individual category members. Prototype strategy use during categorization increased with increasing set coherence, suggesting that coherent training sets facilitate extraction of commonalities within a category. We conclude that learning from a coherent set of examples is an efficient means of forming abstract knowledge that generalizes broadly. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Multivariate neural signatures for health neuroscience: assessing spontaneous regulation during food choice.

Establishing links between neural systems and health can be challenging since there is not a one-to-one mapping between brain regions and psychological states. Building sensitive and specific predictive models of health-relevant constructs using multivariate activation patterns of brain activation is a promising new direction. We illustrate the potential of this approach by building two 'neural signatures' of food craving regulation (CR) using multivariate machine learning and, for comparison, a univariate contrast. We applied the signatures to two large validation samples of overweight adults who completed tasks measuring CR ability and valuation during food choice. Across these samples, the machine learning signature was more reliable. This signature decoded CR from food viewing and higher signature expression was associated with less craving. During food choice, expression of the regulation signature was stronger for unhealthy foods and inversely related to subjective value, indicating that participants engaged in CR despite never being instructed to control their cravings. Neural signatures thus have the potential to measure spontaneous engagement of mental processes in the absence of explicit instruction, affording greater ecological validity. We close by discussing the opportunities and challenges of this approach, emphasizing what machine learning tools bring to the field of health neuroscience.

Brain Mechanisms of Concept Learning.

Concept learning, the ability to extract commonalities and highlight distinctions across a set of related experiences to build organized knowledge, is a critical aspect of cognition. Previous reviews have focused on concept learning research as a means for dissociating multiple brain systems. The current review surveys recent work that uses novel analytical approaches, including the combination of computational modeling with neural measures, focused on testing theories of specific computations and representations that contribute to concept learning. We discuss in detail the roles of the hippocampus, ventromedial prefrontal, lateral prefrontal, and lateral parietal cortices, and how their engagement is modulated by the coherence of experiences and the current learning goals. We conclude that the interaction of multiple brain systems relating to learning, memory, attention, perception, and reward support a flexible concept-learning mechanism that adapts to a range of category structures and incorporates motivational states, making concept learning a fruitful research domain for understanding the neural dynamics underlying complex behaviors.

Differential Functional Connectivity along the Long Axis of the Hippocampus Aligns with Differential Role in Memory Specificity and Generalization.

The hippocampus contributes to both remembering specific events and generalization across events. Recent work suggests that information may be represented along the longitudinal axis of the hippocampus at varied levels of specificity: detailed representations in the posterior hippocampus and generalized representations in the anterior hippocampus. Similar distinctions are thought to exist within neocortex, with lateral prefrontal and lateral parietal regions supporting memory specificity and ventromedial prefrontal and lateral temporal cortices supporting generalized memory. Here, we tested whether functional connectivity of anterior and posterior hippocampus with cortical memory regions is consistent with these proposed dissociations. We predicted greater connectivity of anterior hippocampus with putative generalization regions and posterior hippocampus with putative memory specificity regions. Furthermore, we tested whether differences in connectivity are stable under varying levels of task engagement. Participants learned to categorize a set of stimuli outside the scanner, followed by an fMRI session that included a rest scan, passive viewing runs, and category generalization task runs. Analyses revealed stronger connectivity of ventromedial pFC to anterior hippocampus and of angular gyrus and inferior frontal gyrus to posterior hippocampus. These differences remained relatively stable across the three phases (rest, passive viewing, category generalization). Whole-brain analyses further revealed widespread cortical connectivity with both anterior and posterior hippocampus, with relatively little overlap. These results contribute to our understanding of functional organization along the long axis of the hippocampus and suggest that distinct hippocampal-cortical connections are one mechanism by which the hippocampus represents both individual experiences and generalized knowledge.

Functional connectivity between memory and reward centers across task and rest track memory sensitivity to reward.

External motivation, such as a promise of future monetary reward for remembering an event, can affect which events are remembered. Reward-based memory modulation is thought to result from encoding and post-encoding interactions between dopaminergic midbrain, signaling reward, and hippocampus and parahippocampal cortex, supporting episodic memory. We asked whether hippocampal and parahippocampal interactions with other reward-related regions are related to reward modulation of memory and whether such relationships are stable over time. Individuals' memory sensitivity to reward was measured using a monetary incentive encoding task in which a cue indicated potential monetary reward (penny, dime, or dollar) for remembering an upcoming object pair. Functional connectivity between memory and reward regions was measured before, during, and following the task. Reward-related regions of interest were generated using a meta-analysis of existing studies on reward and included ventral striatum, medial and orbital prefrontal cortices and anterior cingulate cortex, in addition to midbrain. The results showed that connectivity between memory and reward regions tracked individual differences in reward modulation of memory, irrespective of when connectivity was measured. Connectivity patterns of anterior cingulate, orbitofrontal cortex, and ventral striatum covaried together and tracked behavior most strongly. These findings implicate a broader set of reward regions in reward modulation of memory than considered previously and provide new evidence that stable connectivity patterns between memory and reward centers relate to individual differences in how reward impacts memory.

Abstract Representation of Prospective Reward in the Hippocampus.

Motivation enhances memory by increasing hippocampal engagement during encoding. However, whether such increased hippocampal activation reflects encoding of the value of highly rewarding events per se is less understood. Here, using a monetary incentive encoding task with a novel manipulation, we tested in humans whether the hippocampus represents abstract reward value, independent of perceptual content. During functional MRI scanning, men and women studied object pairs, each preceded by a monetary reward cue indicating the amount of money they would receive if they successfully remembered the object pair at test. Reward cues varied on both the level of reward (penny, dime, and dollar) and visual form (picture or word) across trials to dissociate hippocampal responses to reward value from those reflecting the perceptual properties of the cues. Behaviorally, participants remembered pairs associated with the high reward (dollar) more often than pairs associated with lower rewards. Neural pattern-similarity analysis revealed that hippocampal and parahippocampal cortex activation patterns discriminated between cues of different value regardless of their visual form, and that hippocampal discrimination of value was most pronounced in participants who showed the greatest behavioral sensitivity to reward. Strikingly, hippocampal patterns were most distinct for reward cues that differed in value but had similar visual appearance, consistent with theoretical proposals of hippocampal-pattern differentiation of competing representations. Our data illustrate how contextual representations within the hippocampus go beyond space and time to include information about the motivational salience of events, with hippocampal reward coding tracking the motivational impact on later memory.SIGNIFICANCE STATEMENT Motivation, such as the promise of future rewards, enhances hippocampal engagement during encoding and promotes successful retention of events associated with valuable rewards. However, whether the hippocampus explicitly encodes reward value, dissociable from sensory information, is unclear. Here, we show that the hippocampus forms abstract representation of valuable rewards, encoding conceptual rather than perceptual information about the motivational context of individual events. Reward representation within the hippocampus is associated with preferential retention of high-value events in memory. Furthermore, we show that hippocampal-pattern differentiation serves to emphasize differences between visually similar events with distinct motivational salience. Collectively, these findings indicate that hippocampal contextual representations enable individuals to distinguish the motivational value of events, leading to prioritized encoding of significant memories.