The necessity of the medial temporal lobe for statistical learning

Integrating developmental neuroscience with community-engaged approaches to address mental health outcomes for housing-insecure youth: Implications for research, practice, and policy

One in three children in the United States is exposed to insecure housing conditions, including unaffordable, inconsistent, and unsafe housing. These exposures have detrimental impacts on youth mental health. Delineating the neurobehavioral pathways linking exposure to housing insecurity with children's mental health has the potential to inform interventions and policy. However, in approaching this work, carefully considering the lived experiences of youth and families is essential to translating scientific discovery to improve health outcomes in an equitable and representative way. In the current paper, we provide an introduction to the range of stressful experiences that children may face when exposed to insecure housing conditions. Next, we highlight findings from the early-life stress literature regarding the potential neurobehavioral consequences of insecure housing, focusing on how unpredictability is associated with the neural circuitry supporting cognitive and emotional development. We then delineate how community-engaged research (CEnR) approaches have been leveraged to understand the effects of housing insecurity on mental health, and we propose future research directions that integrate developmental neuroscience research and CEnR approaches to maximize the impact of this work. We conclude by outlining practice and policy recommendations that aim to improve the mental health of children exposed to insecure housing.

Visual linguistic statistical learning is traceable through neural entrainment

The human brain can detect statistical regularities in the environment across a wide variety of contexts. The importance of this process is well-established not just in language acquisition but across different modalities; in addition, several neural correlates of statistical learning have been identified. A current technique for tracking the emergence of regularity learning and localizing its neural background is frequency tagging (FT). FT can detect neural entrainment not only to the frequency of stimulus presentation but also to that of a hidden structure. Auditory learning paradigms with linguistic and nonlinguistic stimuli, along with a visual paradigm using nonlinguistic stimuli, have already been tested with FT. To complete the picture, we conducted an FT experiment using written syllables as stimuli and a hidden triplet structure. Both behavioral and neural entrainment data showed evidence of structure learning. In addition, we localized two electrode clusters related to the process, which spread across the frontal and parieto-occipital areas, similar to previous findings. Accordingly, we conclude that fast-paced visual linguistic regularities can be acquired and are traceable through neural entrainment. In comparison with the literature, our findings support the view that statistical learning involves a domain-general network.

Acute Stress Effects on Statistical Learning and Episodic Memory

Stress is widely considered to negatively impact hippocampal function, thus impairing episodic memory. However, the hippocampus is not merely the seat of episodic memory. Rather, it also (via distinct circuitry) supports statistical learning. On the basis of rodent work suggesting that stress may impair the hippocampal pathway involved in episodic memory while sparing or enhancing the pathway involved in statistical learning, we developed a behavioral experiment to investigate the effects of acute stress on both episodic memory and statistical learning in humans. Participants were randomly assigned to one of three conditions: stress (socially evaluated cold pressor) immediately before learning, stress ∼15 min before learning, or no stress. In the learning task, participants viewed a series of trial-unique scenes (allowing for episodic encoding of each image) in which certain scene categories reliably followed one another (allowing for statistical learning of associations between paired categories). Memory was assessed 24 hr later to isolate stress effects on encoding/learning rather than retrieval. We found modest support for our hypothesis that acute stress can amplify statistical learning: Only participants stressed ∼15 min in advance exhibited reliable evidence of learning across multiple measures. Furthermore, stress-induced cortisol levels predicted statistical learning retention 24 hr later. In contrast, episodic memory did not differ by stress condition, although we did find preliminary evidence that acute stress promoted memory for statistically predictable information and attenuated competition between statistical and episodic encoding. Together, these findings provide initial insights into how stress may differentially modulate learning processes within the hippocampus.

Finding Pattern in the Noise: Persistent Implicit Statistical Knowledge Impacts the Processing of Unpredictable Stimuli

Humans can extract statistical regularities of the environment to predict upcoming events. Previous research recognized that implicitly acquired statistical knowledge remained persistent and continued to influence behavior even when the regularities were no longer present in the environment. Here, in an fMRI experiment, we investigated how the persistence of statistical knowledge is represented in the brain. Participants (n = 32) completed a visual, four-choice, RT task consisting of statistical regularities. Two types of blocks constantly alternated with one another throughout the task: predictable statistical regularities in one block type and unpredictable ones in the other. Participants were unaware of the statistical regularities and their changing distribution across the blocks. Yet, they acquired the statistical regularities and showed significant statistical knowledge at the behavioral level not only in the predictable blocks but also in the unpredictable ones, albeit to a smaller extent. Brain activity in a range of cortical and subcortical areas, including early visual cortex, the insula, the right inferior frontal gyrus, and the right globus pallidus/putamen contributed to the acquisition of statistical regularities. The right insula, inferior frontal gyrus, and hippocampus as well as the bilateral angular gyrus seemed to play a role in maintaining this statistical knowledge. The results altogether suggest that statistical knowledge could be exploited in a relevant, predictable context as well as transmitted to and retrieved in an irrelevant context without a predictable structure.

Assessing the impact of attention fluctuations on statistical learning

Attention fluctuates between optimal and suboptimal states. However, whether these fluctuations affect how we learn visual regularities remains untested. Using web-based real-time triggering, we investigated the impact of sustained attentional state on statistical learning using online and offline measures of learning. In three experiments (N = 450), participants performed a continuous performance task (CPT) with shape stimuli. Unbeknownst to participants, we measured response times (RTs) preceding each trial in real time and inserted distinct shape triplets in the trial stream when RTs indicated that a participant was attentive or inattentive. We measured online statistical learning using changes in RTs to regular triplets relative to random triplets encountered in the same attentional states. We measured offline statistical learning with a target detection task in which participants responded to target shapes selected from the regular triplets and with tasks in which participants explicitly re-created the regular triplets or selected regular shapes from foils. Online learning evidence was greater in high vs. low attentional states when combining data from all three experiments, although this was not evident in any experiment alone. On the other hand, we saw no evidence of impacts of attention fluctuations on measures of statistical learning collected offline, after initial exposure in the CPT. These results suggest that attention fluctuations may impact statistical learning while regularities are being extracted online, but that these effects do not persist to subsequent tests of learning about regularities.

Dissociation Between Linguistic and Nonlinguistic Statistical Learning in Children with Autism

Statistical learning (SL), the ability to detect and extract regularities from inputs, is considered a domain-general building block for typical language development. We compared 55 verbal children with autism (ASD, 6-12 years) and 50 typically-developing children in four SL tasks. The ASD group exhibited reduced learning in the linguistic SL tasks (syllable and letter), but showed intact learning for the nonlinguistic SL tasks (tone and image). In the ASD group, better linguistic SL was associated with higher language skills measured by parental report and sentence recall. Therefore, the atypicality of SL in autism is not domain-general but tied to specific processing constraints related to verbal stimuli. Our findings provide a novel perspective for understanding language heterogeneity in autism.

Multiple mechanisms of visual prediction as revealed by the timecourse of scene-object facilitation

Not only semantic, but also recently learned arbitrary associations have the potential to facilitate visual processing in everyday life-for example, knowledge of a (moveable) object's location at a specific time may facilitate visual processing of that object. In our prior work, we showed that previewing a scene can facilitate processing of recently associated objects at the level of visual analysis (Smith and Federmeier in Journal of Cognitive Neuroscience, 32(5):783-803, 2020). In the current study, we assess how rapidly this facilitation unfolds by manipulating scene preview duration. We then compare our results to studies using well-learned object-scene associations in a first-pass assessment of whether systems consolidation might speed up high-level visual prediction. In two ERP experiments (N = 60), we had participants study categorically organized novel object-scene pairs in an explicit paired associate learning task. At test, we varied contextual pre-exposure duration, both between (200 vs. 2500 ms) and within subjects (0-2500 ms). We examined the N300, an event-related potential component linked to high-level visual processing of objects and scenes and found that N300 effects of scene congruity increase with longer scene previews, up to approximately 1-2 s. Similar results were obtained for response times and in a separate component-neutral ERP analysis of visual template matching. Our findings contrast with prior evidence that scenes can rapidly facilitate visual processing of commonly associated objects. This raises the possibility that systems consolidation might mediate different kinds of predictive processing with different temporal profiles.

Beyond the ears: A review exploring the interconnected brain behind the hierarchical memory of music

Music is a ubiquitous element of daily life. Understanding how music memory is represented and expressed in the brain is key to understanding how music can influence human daily cognitive tasks. Current music-memory literature is built on data from very heterogeneous tasks for measuring memory, and the neural correlates appear to differ depending on different forms of memory function targeted. Such heterogeneity leaves many exceptions and conflicts in the data underexplained (e.g., hippocampal involvement in music memory is debated). This review provides an overview of existing neuroimaging results from music-memory related studies and concludes that although music is a special class of event in our lives, the memory systems behind it do in fact share neural mechanisms with memories from other modalities. We suggest that dividing music memory into different levels of a hierarchy (structural level and semantic level) helps understand overlap and divergence in neural networks involved. This is grounded in the fact that memorizing a piece of music recruits brain clusters that separately support functions including-but not limited to-syntax storage and retrieval, temporal processing, prediction versus reality comparison, stimulus feature integration, personal memory associations, and emotion perception. The cross-talk between frontal-parietal music structural processing centers and the subcortical emotion and context encoding areas explains why music is not only so easily memorable but can also serve as strong contextual information for encoding and retrieving nonmusic information in our lives.

The hippocampus and implicit memory (by any other name)

Is the hippocampus involved in implicit memory? I argue that contemporary views on hippocampal function, going beyond the classic dichotomy of explicit versus implicit, predict involvement of the hippocampus whenever flexible, predictive associations are rapidly encoded. This involvement is independent of conscious awareness. A paradigm case is statistical learning: the unconscious extraction of statistical regularities from the environment. In line with this, a substantial body of literature on contextual cueing in visual search has established hippocampal involvement in this form of implicit learning. To conclude, implicit memory (as such or by any other name) is associated with the hippocampus.

Differential effects of bilateral hippocampal CA3 damage on the implicit learning and recognition of complex event sequences

Learning regularities in the environment is a fundament of human cognition, which is supported by a network of brain regions that include the hippocampus. In two experiments, we assessed the effects of selective bilateral damage to human hippocampal subregion CA3, which was associated with autobiographical episodic amnesia extending ~50 years prior to the damage, on the ability to recognize complex, deterministic event sequences presented either in a spatial or a non-spatial configuration. In contrast to findings from related paradigms, modalities, and homologue species, hippocampal damage did not preclude recognition memory for an event sequence studied and tested at four spatial locations, whereas recognition memory for an event sequence presented at a single location was at chance. In two additional experiments, recognition memory for novel single-items was intact, whereas the ability to recognize novel single-items in a different location from that presented at study was at chance. The results are at variance with a general role of the hippocampus in the learning and recognition of complex event sequences based on non-adjacent spatial and temporal dependencies. We discuss the impact of the results on established theoretical accounts of the hippocampal contributions to implicit sequence learning and episodic memory.

Statistical learning in epilepsy: Behavioral and anatomical mechanisms in the human brain

OBJECTIVE

Statistical learning, the fundamental cognitive ability of humans to extract regularities across experiences over time, engages the medial temporal lobe (MTL) in the healthy brain. This leads to the hypothesis that statistical learning (SL) may be impaired in patients with epilepsy (PWE) involving the temporal lobe, and that this impairment could contribute to their varied memory deficits. In turn, studies done in collaboration with PWE, that evaluate the necessity of MTL circuitry through disease and causal perturbations, provide an opportunity to advance basic understanding of SL.

METHODS

We implemented behavioral testing, volumetric analysis of the MTL substructures, and direct electrical brain stimulation to examine SL across a cohort of 61 PWE and 28 healthy controls.

RESULTS

We found that behavioral performance in an SL task was negatively associated with seizure frequency irrespective of seizure origin. The volume of hippocampal subfields CA1 and CA2/3 correlated with SL performance, suggesting a more specific role of the hippocampus. Transient direct electrical stimulation of the hippocampus disrupted SL. Furthermore, the relationship between SL and seizure frequency was selective, as behavioral performance in an episodic memory task was not impacted by seizure frequency.

SIGNIFICANCE

Overall, these results suggest that SL may be hippocampally dependent and that the SL task could serve as a clinically useful behavioral assay of seizure frequency that may complement existing approaches such as seizure diaries. Simple and short SL tasks may thus provide patient-centered endpoints for evaluating the efficacy of novel treatments in epilepsy.

The successor representation subserves hierarchical abstraction for goal-directed behavior

Humans have the ability to craft abstract, temporally extended and hierarchically organized plans. For instance, when considering how to make spaghetti for dinner, we typically concern ourselves with useful "subgoals" in the task, such as cutting onions, boiling pasta, and cooking a sauce, rather than particulars such as how many cuts to make to the onion, or exactly which muscles to contract. A core question is how such decomposition of a more abstract task into logical subtasks happens in the first place. Previous research has shown that humans are sensitive to a form of higher-order statistical learning named "community structure". Community structure is a common feature of abstract tasks characterized by a logical ordering of subtasks. This structure can be captured by a model where humans learn predictions of upcoming events multiple steps into the future, discounting predictions of events further away in time. One such model is the "successor representation", which has been argued to be useful for hierarchical abstraction. As of yet, no study has convincingly shown that this hierarchical abstraction can be put to use for goal-directed behavior. Here, we investigate whether participants utilize learned community structure to craft hierarchically informed action plans for goal-directed behavior. Participants were asked to search for paintings in a virtual museum, where the paintings were grouped together in "wings" representing community structure in the museum. We find that participants' choices accord with the hierarchical structure of the museum and that their response times are best predicted by a successor representation. The degree to which the response times reflect the community structure of the museum correlates with several measures of performance, including the ability to craft temporally abstract action plans. These results suggest that successor representation learning subserves hierarchical abstractions relevant for goal-directed behavior.

The effect of hippocampal subfield damage on rapid temporal integration through statistical learning and associative inference

INTRODUCTION

The hippocampus (HPC) supports integration of information across time, often indexed by associative inference (AI) and statistical learning (SL) tasks. In AI, an indirect association between stimuli that never appeared together is inferred, whereas SL involves learning item relationships by extracting regularities across experiences. A recent model of hippocampal function (Schapiro et al., 2017) proposes that the HPC can support temporal integration in both paradigms through its two distinct pathways.

METHODS

We tested this models' predictions in four patients with varying degrees of bilateral HPC damage and matched healthy controls, with two patients with complementary damage to either the monosynaptic or trisynaptic pathway. During AI, participants studied overlapping paired associates (AB, BC) and their memory was tested for premise pairs (AB) and for inferred pairs (AC). During SL, participants passively viewed a continuous picture sequence that contained an underlying structure of triplets that later had to be recognized.

RESULTS

Binomial distributions were used to calculate above chance performance at the individual level. For AI, patients with focal HPC damage were impaired at inference but could correctly infer pairs above chance once premise pair acquisition was equated to controls; however, the patient with HPC and cortical damage showed severe impairment at recalling premise and inferred pairs, regardless of accounting for premise pair performance. For SL, none of the patients performed above chance, but notably neither did most controls.

CONCLUSIONS

Associative inference of indirect relationships can be intact with HPC damage to either hippocampal pathways or the HPC more broadly, provided premise pairs can first be formed. Inference may remain intact through residual HPC tissue supporting premise pair acquisition, and/or through extra-hippocampal structures supporting inference at retrieval. Clear conclusions about hippocampal contributions to SL are precluded by low performance in controls, which we caution is not dissimilar to previous amnesic studies using the same task. This complicates interpretations of studies claiming necessity of hippocampal contributions to SL and warrants the use of a common and reliable task before conclusions can be drawn.

Distinguishing examples while building concepts in hippocampal and artificial networks

The hippocampal subfield CA3 is thought to function as an auto-associative network that stores experiences as memories. Information from these experiences arrives directly from the entorhinal cortex as well as indirectly through the dentate gyrus, which performs sparsification and decorrelation. The computational purpose for these dual input pathways has not been firmly established. We model CA3 as a Hopfield-like network that stores both dense, correlated encodings and sparse, decorrelated encodings. As more memories are stored, the former merge along shared features while the latter remain distinct. We verify our model's prediction in rat CA3 place cells, which exhibit more distinct tuning during theta phases with sparser activity. Finally, we find that neural networks trained in multitask learning benefit from a loss term that promotes both correlated and decorrelated representations. Thus, the complementary encodings we have found in CA3 can provide broad computational advantages for solving complex tasks.

Of words and whistles: Statistical learning operates similarly for identical sounds perceived as speech and non-speech

Statistical learning is an ability that allows individuals to effortlessly extract patterns from the environment, such as sound patterns in speech. Some prior evidence suggests that statistical learning operates more robustly for speech compared to non-speech stimuli, supporting the idea that humans are predisposed to learn language. However, any apparent statistical learning advantage for speech could be driven by signal acoustics, rather than the subjective perception per se of sounds as speech. To resolve this issue, the current study assessed whether there is a statistical learning advantage for ambiguous sounds that are subjectively perceived as speech-like compared to the same sounds perceived as non-speech, thereby controlling for acoustic features. We first induced participants to perceive sine-wave speech (SWS)-a degraded form of speech not immediately perceptible as speech-as either speech or non-speech. After this induction phase, participants were exposed to a continuous stream of repeating trisyllabic nonsense words, composed of SWS syllables, and then completed an explicit familiarity rating task and an implicit target detection task to assess learning. Critically, participants showed robust and equivalent performance on both measures, regardless of their subjective speech perception. In contrast, participants who perceived the SWS syllables as more speech-like showed better detection of individual syllables embedded in speech streams. These results suggest that speech perception facilitates processing of individual sounds, but not the ability to extract patterns across sounds. Our findings suggest that statistical learning is not influenced by the perceived linguistic relevance of sounds, and that it may be conceptualized largely as an automatic, stimulus-driven mechanism.

Multidimensional assessment of time perception along the continuum of Alzheimer's Disease and evidence of alterations in subjective cognitive decline

Timing alterations occur in Alzheimer's disease (AD), even in early stages (mild cognitive impairment, MCI). Moreover, a stage named subjective cognitive decline (SCD), in which individuals perceive a change in cognitive performance not revealed by neuropsychological tests, has been identified as a preclinical phase of AD. However, no study to date has investigated different dimensions of time processing along the continuum from physiological to pathological aging, and whether timing alterations occur in SCD. Here a sample of participants with SCD, MCI, AD and healthy controls (HC) performed tasks assessing prospective duration estimation, production, reproduction, implicit temporal learning in conditions dependent from external cues (externally-cued learning, ECL) or independent from external cues (internally-based learning, IBL), retrospective duration estimation, the subjective experience of time and the temporal collocation of events. AD patients performed worse than HC and SCD in prospective timing, and in collocating events in time. The subjective experience of time did not differ between groups. Concerning temporal learning, AD performed worse in ECL than in IBL, whereas SCD performed worse in IBL than in ECL. SCD, MCI and AD patients all showed errors greater than HC in retrospective duration estimation. Results point to implicit temporal learning in externally-cued conditions and retrospective time estimation as possible early markers of cognitive decline.

Implicit auditory memory in older listeners: From encoding to 6-month retention

Any listening task, from sound recognition to sound-based communication, rests on auditory memory which is known to decline in healthy ageing. However, how this decline maps onto multiple components and stages of auditory memory remains poorly characterised. In an online unsupervised longitudinal study, we tested ageing effects on implicit auditory memory for rapid tone patterns. The test required participants (younger, aged 20-30, and older adults aged 60-70) to quickly respond to rapid regularly repeating patterns emerging from random sequences. Patterns were novel in most trials (REGn), but unbeknownst to the participants, a few distinct patterns reoccurred identically throughout the sessions (REGr). After correcting for processing speed, the response times (RT) to REGn should reflect the information held in echoic and short-term memory before detecting the pattern; long-term memory formation and retention should be reflected by the RT advantage (RTA) to REGr vs REGn which is expected to grow with exposure. Older participants were slower than younger adults in detecting REGn and exhibited a smaller RTA to REGr. Computational simulations using a model of auditory sequence memory indicated that these effects reflect age-related limitations both in early and long-term memory stages. In contrast to ageing-related accelerated forgetting of verbal material, here older adults maintained stable memory traces for REGr patterns up to 6 months after the first exposure. The results demonstrate that ageing is associated with reduced short-term memory and long-term memory formation for tone patterns, but not with forgetting, even over surprisingly long timescales.

Hopfield-like network with complementary encodings of memories

We present a Hopfield-like autoassociative network for memories representing examples of concepts. Each memory is encoded by two activity patterns with complementary properties. The first is dense and correlated across examples within concepts, and the second is sparse and exhibits no correlation among examples. The network stores each memory as a linear combination of its encodings. During retrieval, the network recovers sparse or dense patterns with a high or low activity threshold, respectively. As more memories are stored, the dense representation at low threshold shifts from examples to concepts, which are learned from accumulating common example features. Meanwhile, the sparse representation at high threshold maintains distinctions between examples due to the high capacity of sparse, decorrelated patterns. Thus, a single network can retrieve memories at both example and concept scales and perform heteroassociation between them. We obtain our results by deriving macroscopic mean-field equations that yield capacity formulas for sparse examples, dense examples, and dense concepts. We also perform simulations that verify our theoretical results and explicitly demonstrate the capabilities of the network.

Intact procedural memory and impaired auditory statistical learning in adults with dyslexia

Developmental dyslexia is a reading disorder that is associated with atypical brain function. One neuropsychological theory posits that dyslexia reflects a deficit in the procedural memory system, which supports implicit learning, or the acquisition of knowledge without conscious awareness or intention. This study investigated various forms of procedural learning in adults with dyslexia and typically-reading adults. Adults with dyslexia exhibited typical skill learning on mirror tracing and rotary pursuit tasks that have been well-established as reflecting purely procedural memory and dependent on basal ganglia and cerebellar structures. They also exhibited typical statistical learning for visual material, but impaired statistical learning for auditory material. Auditory statistical learning proficiency correlated positively with single-word reading performance across all participants and within the group with dyslexia, linking a major difficulty in dyslexia with impaired auditory statistical learning. These findings dissociate multiple forms of procedural memory that are intact in dyslexia from a specific impairment in auditory statistical learning that is associated with reading difficulty.

Conscious awareness and memory systems in the brain

The term "memory" typically refers to conscious retrieval of events and experiences from our past, but experience can also change our behaviour without corresponding awareness of the learning process or the associated outcome. Based primarily on early neuropsychological work, theoretical perspectives have distinguished between conscious memory, said to depend critically on structures in the medial temporal lobe (MTL), and a collection of performance-based memories that do not. The most influential of these memory systems perspectives, the declarative memory theory, continues to be a mainstay of scientific work today despite mounting evidence suggesting that contributions of MTL structures go beyond the kinds or types of memory that can be explicitly reported. Consistent with these reports, more recent perspectives have focused increasingly on the processing operations supported by particular brain regions and the qualities or characteristics of resulting representations whether memory is expressed with or without awareness. These alternatives to the standard model generally converge on two key points. First, the hippocampus is critical for relational memory binding and representation even without awareness and, second, there may be little difference between some types of priming and explicit, familiarity-based recognition. Here, we examine the evolution of memory systems perspectives and critically evaluate scientific evidence that has challenged the status quo. Along the way, we highlight some of the challenges that researchers encounter in the context of this work, which can be contentious, and describe innovative methods that have been used to examine unconscious memory in the lab. This article is categorized under: Psychology > Memory Psychology > Theory and Methods Philosophy > Consciousness.

Memories of structured input become increasingly distorted across development

Trajectories of cognitive and neural development suggest that, despite early emergence, the ability to extract environmental patterns changes across childhood. Here, 5- to 9-year-olds and adults (N = 211, 110 females, in a large Canadian city) completed a memory test assessing what they remembered after watching a stream of shape triplets: the particular sequence in which the shapes occurred and/or their group-level structure. After accounting for developmental improvements in overall memory, all ages remembered specific transitions, while memory for group membership was only observed in older children and adults (age by test-type interaction η2  = .05). Thus, while young children form memories for specifics of structured experience, memory for derived associations is refined later-underscoring that adults and young children form different memories despite identical experience.

The locus of recognition memory signals in human cortex depends on the complexity of the memory representations

According to a "Swiss Army Knife" model of the brain, cognitive functions such as episodic memory and face perception map onto distinct neural substrates. In contrast, representational accounts propose that each brain region is best explained not by which specialized function it performs, but by the type of information it represents with its neural firing. In a functional magnetic resonance imaging study, we asked whether the neural signals supporting recognition memory fall mandatorily within the medial temporal lobes (MTL), traditionally thought the seat of declarative memory, or whether these signals shift within cortex according to the content of the memory. Participants studied objects and scenes that were unique conjunctions of pre-defined visual features. Next, we tested recognition memory in a task that required mnemonic discrimination of both simple features and complex conjunctions. Feature memory signals were strongest in posterior visual regions, declining with anterior progression toward the MTL, while conjunction memory signals followed the opposite pattern. Moreover, feature memory signals correlated with feature memory discrimination performance most strongly in posterior visual regions, whereas conjunction memory signals correlated with conjunction memory discrimination most strongly in anterior sites. Thus, recognition memory signals shifted with changes in memory content, in line with representational accounts.

Network structure influences the strength of learned neural representations

Human experience is built upon sequences of discrete events. From those sequences, humans build impressively accurate models of their world. This process has been referred to as graph learning, a form of structure learning in which the mental model encodes the graph of event-to-event transition probabilities [1], [2], typically in medial temporal cortex [3]-[6]. Recent evidence suggests that some network structures are easier to learn than others [7]-[9], but the neural properties of this effect remain unknown. Here we use fMRI to show that the network structure of a temporal sequence of stimuli influences the fidelity with which those stimuli are represented in the brain. Healthy adult human participants learned a set of stimulus-motor associations following one of two graph structures. The design of our experiment allowed us to separate regional sensitivity to the structural, stimulus, and motor response components of the task. As expected, whereas the motor response could be decoded from neural representations in postcentral gyrus, the shape of the stimulus could be decoded from lateral occipital cortex. The structure of the graph impacted the nature of neural representations: when the graph was modular as opposed to lattice-like, BOLD representations in visual areas better predicted trial identity in a held-out run and displayed higher intrinsic dimensionality. Our results demonstrate that even over relatively short timescales, graph structure determines the fidelity of event representations as well as the dimensionality of the space in which those representations are encoded. More broadly, our study shows that network context influences the strength of learned neural representations, motivating future work in the design, optimization, and adaptation of network contexts for distinct types of learning over different timescales.

A case of severe anterograde amnesia in the era of smartphone technology

A.V. is a young herpes simplex encephalitis (HSE) survivor who suffered extensive bilateral damage to the medial temporal lobe (MTL) leading to a severe and pervasive form of anterograde amnesia. Structural Magnetic Resonance Imaging (MRI) revealed lesions that encompass the hippocampus and amygdala in both hemispheres and that extend more laterally in the right temporal lobe. At the same time, detailed neuropsychological testing showed that the disparity between A.V.'s preserved intellectual functioning (Full Scale IQ: 115) and severe memory deficit (Delayed Memory Index: 42) is one of the largest on record. Despite this deficit, A.V. has regained a higher level of functioning and autonomy compared to previously documented amnesic cases with major bilateral MTL lesions. As a millennial, one advantage which A.V. has over prior amnesic cases is fluency with digital technology - particularly the smartphone. The analysis of his phone and specific app usage showed a pattern that is consistent with the strategy to offload cognitive tasks that would normally be supported by the MTL. A.V.'s behavior is significant in terms of rehabilitation and may have broader implications at the societal level and for public health given the ubiquity of smartphone technology and its potential to become integrated with neural mnemonic functions.

Barcoding of episodic memories in the hippocampus of a food-caching bird

Episodic memory, or memory of experienced events, is a critical function of the hippocampus1-3. It is therefore important to understand how hippocampal activity represents specific events in an animal's life. We addressed this question in chickadees - specialist food-caching birds that hide food at scattered locations and use memory to find their caches later in time4,5. We performed high-density neural recordings in the hippocampus of chickadees as they cached and retrieved seeds in a laboratory arena. We found that each caching event was represented by a burst of firing in a unique set of hippocampal neurons. These 'barcode-like' patterns of activity were sparse (<10% of neurons active), uncorrelated even for immediately adjacent caches, and different even for separate caches at the same location. The barcode representing a specific caching event was transiently reactivated whenever a bird later interacted with the same cache - for example, to retrieve food. Barcodes co-occurred with conventional place cell activity6,7, as well as location-independent responses to cached seeds. We propose that barcodes are signatures of episodic memories evoked during memory recall. These patterns assign a unique identifier to each event and may be a mechanism for rapid formation and storage of many non-interfering memories.

Statistical learning in epilepsy: Behavioral, anatomical, and causal mechanisms in the human brain

Statistical learning, the fundamental cognitive ability of humans to extract regularities across experiences over time, engages the medial temporal lobe in the healthy brain. This leads to the hypothesis that statistical learning may be impaired in epilepsy patients, and that this impairment could contribute to their varied memory deficits. In turn, epilepsy patients provide a platform to advance basic understanding of statistical learning by helping to evaluate the necessity of medial temporal lobe circuitry through disease and causal perturbations. We implemented behavioral testing, volumetric analysis of the medial temporal lobe substructures, and direct electrical brain stimulation to examine statistical learning across a cohort of 61 epilepsy patients and 28 healthy controls. Behavioral performance in a statistical learning task was negatively associated with seizure frequency, irrespective of where seizures originated in the brain. The volume of hippocampal subfields CA1 and CA2/3 correlated with statistical learning performance, suggesting a more specific role of the hippocampus. Indeed, transient direct electrical stimulation of the hippocampus disrupted statistical learning. Furthermore, the relationship between statistical learning and seizure frequency was selective: behavioral performance in an episodic memory task was impacted by structural lesions in the medial temporal lobe and by antiseizure medications, but not by seizure frequency. Overall, these results suggest that statistical learning may be hippocampally dependent and that this task could serve as a clinically useful behavioral assay of seizure frequency distinct from existing neuropsychological tests. Simple and short statistical learning tasks may thus provide patient-centered endpoints for evaluating the efficacy of novel treatments in epilepsy.

What sticks after statistical learning: The persistence of implicit versus explicit memory traces

Statistical learning is a powerful mechanism that extracts even subtle regularities from our information-dense worlds. Recent theories argue that statistical learning can occur through multiple mechanisms-both the conventionally assumed automatic process that precipitates unconscious learning, and an attention-dependent process that brings regularities into conscious awareness. While this view has gained popularity, there are few empirical dissociations of the hypothesized implicit and explicit forms of statistical learning. Here we provide strong evidence for this dissociation in two ways. First, we show in healthy adults (N = 60) that implicit and explicit traces have divergent consolidation trajectories, with implicit knowledge of structure strengthened over a 24-h period, while precise explicit representations tend to decay. Second, we demonstrate that repeated testing strengthens the retention of explicit representations but that implicit statistical learning is uninfluenced by testing. Together these dissociations provide much needed support for the reconceptualization of statistical learning as a multi-component construct.

Aging effects and feasibility of statistical learning tasks across modalities

Knowledge on statistical learning (SL) in healthy elderly is scarce. Theoretically, it is not clear whether aging affects modality-specific and/or domain-general learning mechanisms. Practically, there is a lack of research on simplified SL tasks, which would ease the burden of testing in clinical populations. Against this background, we conducted two experiments across three modalities (auditory, visual and visuomotor) in a total of 93 younger and older adults. In Experiment 1, SL was induced in all modalities. Aging effects appeared in the tasks relying on an explicit posttest to assess SL. We hypothesize that declines in domain-general processes that predominantly modulate explicit learning mechanisms underlie these aging effects. In Experiment 2, more feasible tasks were developed for which the level of SL was maintained in all modalities, except the auditory modality. These tasks are more likely to successfully measure SL in elderly (patient) populations in which task demands can be problematic.

Representational integration and differentiation in the human hippocampus following goal-directed navigation

As we learn, dynamic memory processes build structured knowledge across our experiences. Such knowledge enables the formation of internal models of the world that we use to plan, make decisions, and act. Recent theorizing posits that mnemonic mechanisms of differentiation and integration - which at one level may seem to be at odds - both contribute to the emergence of structured knowledge. We tested this possibility using fMRI as human participants learned to navigate within local and global virtual environments over the course of 3 days. Pattern similarity analyses on entorhinal cortical and hippocampal patterns revealed evidence that differentiation and integration work concurrently to build local and global environmental representations, and that variability in integration relates to differences in navigation efficiency. These results offer new insights into the neural machinery and the underlying mechanisms that translate experiences into structured knowledge that allows us to navigate to achieve goals.

The hearing hippocampus

The hippocampus has a well-established role in spatial and episodic memory but a broader function has been proposed including aspects of perception and relational processing. Neural bases of sound analysis have been described in the pathway to auditory cortex, but wider networks supporting auditory cognition are still being established. We review what is known about the role of the hippocampus in processing auditory information, and how the hippocampus itself is shaped by sound. In examining imaging, recording, and lesion studies in species from rodents to humans, we uncover a hierarchy of hippocampal responses to sound including during passive exposure, active listening, and the learning of associations between sounds and other stimuli. We describe how the hippocampus' connectivity and computational architecture allow it to track and manipulate auditory information - whether in the form of speech, music, or environmental, emotional, or phantom sounds. Functional and structural correlates of auditory experience are also identified. The extent of auditory-hippocampal interactions is consistent with the view that the hippocampus makes broad contributions to perception and cognition, beyond spatial and episodic memory. More deeply understanding these interactions may unlock applications including entraining hippocampal rhythms to support cognition, and intervening in links between hearing loss and dementia.

The role of the hippocampus in statistical learning and language recovery in persons with post stroke aphasia

Although several studies have aimed for accurate predictions of language recovery in post stroke aphasia, individual language outcomes remain hard to predict. Large-scale prediction models are built using data from patients mainly in the chronic phase after stroke, although it is clinically more relevant to consider data from the acute phase. Previous research has mainly focused on deficits, i.e., behavioral deficits or specific brain damage, rather than compensatory mechanisms, i.e., intact cognitive skills or undamaged brain regions. One such unexplored brain region that might support language (re)learning in aphasia is the hippocampus, a region that has commonly been associated with an individual's learning potential, including statistical learning. This refers to a set of mechanisms upon which we rely heavily in daily life to learn a range of regularities across cognitive domains. Against this background, thirty-three patients with aphasia (22 males and 11 females, M = 69.76 years, SD = 10.57 years) were followed for 1 year in the acute (1-2 weeks), subacute (3-6 months) and chronic phase (9-12 months) post stroke. We evaluated the unique predictive value of early structural hippocampal measures for short-term and long-term language outcomes (measured by the ANELT). In addition, we investigated whether statistical learning abilities were intact in patients with aphasia using three different tasks: an auditory-linguistic and visual task based on the computation of transitional probabilities and a visuomotor serial reaction time task. Finally, we examined the association of individuals' statistical learning potential with acute measures of hippocampal gray and white matter. Using Bayesian statistics, we found moderate evidence for the contribution of left hippocampal gray matter in the acute phase to the prediction of long-term language outcomes, over and above information on the lesion and the initial language deficit (measured by the ScreeLing). Non-linguistic statistical learning in patients with aphasia, measured in the subacute phase, was intact at the group level compared to 23 healthy older controls (8 males and 15 females, M = 74.09 years, SD = 6.76 years). Visuomotor statistical learning correlated with acute hippocampal gray and white matter. These findings reveal that particularly left hippocampal gray matter in the acute phase is a potential marker of language recovery after stroke, possibly through its statistical learning ability.

Remembering the pattern: A longitudinal case study on statistical learning in spatial navigation and memory consolidation

Distinct brain systems are thought to support statistical learning over different timescales. Regularities encountered during online perceptual experience can be acquired rapidly by the hippocampus. Further processing during offline consolidation can establish these regularities gradually in cortical regions, including the medial prefrontal cortex (mPFC). These mechanisms of statistical learning may be critical during spatial navigation, for which knowledge of the structure of an environment can facilitate future behavior. Rapid acquisition and prolonged retention of regularities have been investigated in isolation, but how they interact in the context of spatial navigation is unknown. We had the rare opportunity to study the brain systems underlying both rapid and gradual timescales of statistical learning using intracranial electroencephalography (iEEG) longitudinally in the same patient over a period of three weeks. As hypothesized, spatial patterns were represented in the hippocampus but not mPFC for up to one week after statistical learning and then represented in the mPFC but not hippocampus two and three weeks after statistical learning. Taken together, these findings suggest that the hippocampus may contribute to the initial extraction of regularities prior to cortical consolidation.

Representations of Temporal Community Structure in Hippocampus and Precuneus Predict Inductive Reasoning Decisions

Our understanding of the world is shaped by inferences about underlying structure. For example, at the gym, you might notice that the same people tend to arrive around the same time and infer that they are friends that work out together. Consistent with this idea, after participants are presented with a temporal sequence of objects that follows an underlying community structure, they are biased to infer that objects from the same community share the same properties. Here, we used fMRI to measure neural representations of objects after temporal community structure learning and examine how these representations support inference about object relationships. We found that community structure learning affected inferred object similarity: When asked to spatially group items based on their experience, participants tended to group together objects from the same community. Neural representations in perirhinal cortex predicted individual differences in object grouping, suggesting that high-level object representations are affected by temporal community learning. Furthermore, participants were biased to infer that objects from the same community would share the same properties. Using computational modeling of temporal learning and inference decisions, we found that inductive reasoning is influenced by both detailed knowledge of temporal statistics and abstract knowledge of the temporal communities. The fidelity of temporal community representations in hippocampus and precuneus predicted the degree to which temporal community membership biased reasoning decisions. Our results suggest that temporal knowledge is represented at multiple levels of abstraction, and that perirhinal cortex, hippocampus, and precuneus may support inference based on this knowledge.

Repeated series learning revisited with a novel prediction on the reduced effect of item frequency in dyslexia

Developmental dyslexia, a difficulty with acquiring fluent reading, has also been characterized by reduced short-term memory (STM) capacity, which is often operationalized with span tasks. The low performance of individuals with dyslexia (IDDs) in such tasks is commonly attributed to poor phonological memory. However, we suggest an alternative explanation based on the observation that many times the items that are used in spans tasks are high-frequency items (e.g., digit words). We suggest that IDDs do not enjoy the benefit of item frequency to the same extent as controls, and thus their performance in span tasks is especially hampered. On the contrary, learning of repeated sequences was shown to be largely independent of item frequency, and therefore this type of learning may be unimpaired in dyslexia. To test both predictions, we used the Hebb-learning paradigm. We found that IDDs’ performance is especially poor compared to controls’ when high-frequency items are used, and that their repeated series learning does not differ from that of controls. Taken together with existing literature, our findings suggest that impaired learning of repeated series is not a core characteristic of dyslexia, and that the reports on reduced STM in dyslexia may to a large extent be explained by reduced benefit of item frequency.

Contextual Expectations Shape Cortical Reinstatement of Sensory Representations

When making a turn at a familiar intersection, we know what items and landmarks will come into view. These perceptual expectations, or predictions, come from our knowledge of the context; however, it is unclear how memory and perceptual systems interact to support the prediction and reactivation of sensory details in cortex. To address this, human participants learned the spatial layout of animals positioned in a cross maze. During fMRI, participants of both sexes navigated between animals to reach a target, and in the process saw a predictable sequence of five animal images. Critically, to isolate activity patterns related to item predictions, rather than bottom-up inputs, one-fourth of trials ended early, with a blank screen presented instead. Using multivariate pattern similarity analysis, we reveal that activity patterns in early visual cortex, posterior medial regions, and the posterior hippocampus showed greater similarity when seeing the same item compared with different items. Further, item effects in posterior hippocampus were specific to the sequence context. Critically, activity patterns associated with seeing an item in visual cortex and posterior medial cortex, were also related to activity patterns when an item was expected, but omitted, suggesting sequence predictions were reinstated in these regions. Finally, multivariate connectivity showed that patterns in the posterior hippocampus at one position in the sequence were related to patterns in early visual cortex and posterior medial cortex at a later position. Together, our results support the idea that hippocampal representations facilitate sensory processing by modulating visual cortical activity in anticipation of expected items.SIGNIFICANCE STATEMENT Our visual world is a series of connected events, where we can predict what we might see next based on our recent past. Understanding the neural circuitry and mechanisms of the perceptual and memory systems that support these expectations is fundamental to revealing how we perceive and act in our world. Using brain imaging, we studied what happens when we expect to see specific visual items, and how such expectations relate to top-down memory signals. We find both visual and memory systems reflect item predictions, and moreover, we show that hippocampal activity supports predictions of future expected items. This demonstrates that the hippocampus acts to predict upcoming items, and reinstates such predictions in cortex.

Perceptual pathways to hallucinogenesis

Recent advances in computational psychiatry have provided unique insights into the neural and cognitive underpinnings of psychotic symptoms. In particular, a host of new data has demonstrated the utility of computational frameworks for understanding how hallucinations might arise from alterations in typical perceptual processing. Of particular promise are models based in Bayesian inference that link hallucinatory perceptual experiences to latent states that may drive them. In this piece, we move beyond these findings to ask: how and why do these latent states arise, and how might we take advantage of heterogeneity in that process to develop precision approaches to the treatment of hallucinations? We leverage specific models of Bayesian inference to discuss components that might lead to the development of hallucinations. Using the unifying power of our model, we attempt to place disparate findings in the study of psychotic symptoms within a common framework. Finally, we suggest directions for future elaboration of these models in the service of a more refined psychiatric nosology based on predictable, testable, and ultimately treatable information processing derangements.

Motor response vigour and visual fixation patterns reflect subjective valuation during intertemporal choice

Value-based decision-making is of central interest in cognitive neuroscience and psychology, as well as in the context of neuropsychiatric disorders characterised by decision-making impairments. Studies examining (neuro-)computational mechanisms underlying choice behaviour typically focus on participants’ decisions. However, there is increasing evidence that option valuation might also be reflected in motor response vigour and eye movements, implicit measures of subjective utility. To examine motor response vigour and visual fixation correlates of option valuation in intertemporal choice, we set up a task where the participants selected an option by pressing a grip force transducer, simultaneously tracking fixation shifts between options. As outlined in our preregistration (https://osf.io/k6jct), we used hierarchical Bayesian parameter estimation to model the choices assuming hyperbolic discounting, compared variants of the softmax and drift diffusion model, and assessed the relationship between response vigour and the estimated model parameters. The behavioural data were best explained by a drift diffusion model specifying a non-linear scaling of the drift rate by the subjective value differences. Replicating previous findings, we found a magnitude effect for temporal discounting, such that higher rewards were discounted less. This magnitude effect was further reflected in motor response vigour, such that stronger forces were exerted in the high vs. the low magnitude condition. Bayesian hierarchical linear regression further revealed higher grip forces, faster response times and a lower number of fixation shifts for trials with higher subjective value differences. An exploratory analysis revealed that subjective value sums across options showed an even more pronounced association with trial-wise grip force amplitudes. Our data suggest that subjective utility or implicit valuation is reflected in motor response vigour and visual fixation patterns during intertemporal choice. Taking into account response vigour might thus provide deeper insight into decision-making, reward valuation and maladaptive changes in these processes, e.g. in the context of neuropsychiatric disorders.

Hippocampal representations switch from errors to predictions during acquisition of predictive associations

We constantly exploit the statistical regularities in our environment to help guide our perception. The hippocampus has been suggested to play a pivotal role in both learning environmental statistics, as well as exploiting them to generate perceptual predictions. However, it is unclear how the hippocampus balances encoding new predictive associations with the retrieval of existing ones. Here, we present the results of two high resolution human fMRI studies (N = 24 for both experiments) directly investigating this. Participants were exposed to auditory cues that predicted the identity of an upcoming visual shape (with 75% validity). Using multivoxel decoding analysis, we find that the hippocampus initially preferentially represents unexpected shapes (i.e., those that violate the cue regularities), but later switches to representing the cue-predicted shape regardless of which was actually presented. These findings demonstrate that the hippocampus is involved both acquiring and exploiting predictive associations, and is dominated by either errors or predictions depending on whether learning is ongoing or complete.

Caught in the ACTS: Defining Abstract Cognitive Task Sequences as an Independent Process

Cognitive neuroscience currently conflates the study of serial responses (e.g., delay match to sample/nonsample, n-back) with the study of sequential operations. In this essay, our goal is to define and disentangle the latter, termed abstract cognitive task sequences (ACTS). Existing literatures address tasks requiring serial events, including procedural learning of implicit motor responses, statistical learning of predictive relationships, and judgments of attributes. These findings do not describe the behavior and underlying mechanism required to succeed at remembering to evaluate color, then shape; or to multiply, then add. A new literature is needed to characterize these sorts of second-order cognitive demands of studying a sequence of operations. Our second goal is to characterize gaps in knowledge related to ACTS that merit further investigation. In the following sections, we define more precisely what we mean by ACTS and suggest research questions' further investigation would be positioned to address.

Brain activity patterns underlying memory confidence

The primary aim of this review is to examine the brain activity patterns that are related to subjectively perceived memory confidence. We focus on the main brain regions involved in episodic memory: the medial temporal lobe (MTL), prefrontal cortex (PFC), and posterior parietal cortex (PPC), and relate activity in their subregions to memory confidence. How this brain activity in both the encoding and retrieval phase is related to (subsequent) memory confidence ratings will be discussed. Specifically, encoding related activity in MTL regions and ventrolateral PFC mainly shows a positive linear increase with subsequent memory confidence, while dorsolateral and ventromedial PFC activity show mixed patterns. In addition, encoding-related PPC activity seems to only have indirect effects on memory confidence ratings. Activity during retrieval in both the hippocampus and parahippocampal cortex increases with memory confidence, especially during high-confident recognition. Retrieval-related activity in the PFC and PPC show mixed relationships with memory confidence, likely related to post-retrieval monitoring and attentional processes, respectively. In this review, these MTL, PFC, and PPC activity patterns are examined in detail and related to their functional roles in memory processes. This insight into brain activity that underlies memory confidence is important for our understanding of brain-behaviour relations and memory-guided decision making.

Sleeping neonates track transitional probabilities in speech but only retain the first syllable of words

Extracting statistical regularities from the environment is a primary learning mechanism that might support language acquisition. While it has been shown that infants are sensitive to transition probabilities between syllables in speech, it is still not known what information they encode. Here we used electrophysiology to study how full-term neonates process an artificial language constructed by randomly concatenating four pseudo-words and what information they retain after a few minutes of exposure. Neural entrainment served as a marker of the regularities the brain was tracking during learning. Then in a post-learning phase, evoked-related potentials (ERP) to different triplets explored which information was retained. After two minutes of familiarization with the artificial language, neural entrainment at the word rate emerged, demonstrating rapid learning of the regularities. ERPs in the test phase significantly differed between triplets starting or not with the correct first syllables, but no difference was associated with subsequent violations in transition probabilities. Thus, our results revealed a two-step learning process: neonates segmented the stream based on its statistical regularities, but memory encoding targeted during the word recognition phase entangled the ordinal position of the syllables but was still incomplete at that age.

Statistical word learning in Catalan-Spanish and English-speaking children with and without developmental language disorder

BACKGROUND

A growing body of work shows that children with developmental language disorder (DLD) perform poorly on statistical word learning (SWL) tasks, consistent with the predictions of the Procedural Deficit Hypothesis that predicts that procedural memory is impaired in DLD. To date, however, SWL performance has not been compared across linguistically heterogeneous populations of children with DLD.

AIMS

To compare SWL performance in a group of age, sex and non-verbal IQ-matched Catalan-Spanish and English-speaking children with and without DLD.

METHODS & PROCEDURES

Two cohorts of children: (1) 35 Catalan-Spanish-speaking children with DLD (M_age = 8;7 years) and 35 age/sex-matched typical developing (TD) children (M_age = 8;9 years), and (2) 24 English-speaking children with DLD (M_age = 9;1 years) and 19 age/sex matched TD controls (M_age = 8;9 years) completed the tone version of a SWL task from Evans et al. (2009). Children listened to a tone language in which transitional probabilities within tone words were higher than those between words.

OUTCOMES & RESULTS

For both Catalan-Spanish and English cohorts, overall performance for the children with DLD was poorer than that of the TD controls regardless of the child's native language. Item analysis revealed that children with DLD had difficulty tracking statistical information and using transitional probability to discover tone word boundaries within the input. For both the Catalan-Spanish and English-speaking children, SWL accounted for a significant amount of unique variance in Receptive and Expressive vocabulary. Likelihood ratio analysis revealed that for both Catalan-Spanish and English cohorts, children having performance ≤ 45% on the SWL task had an extremely high degree of likelihood of having DLD. The analysis also revealed that for the Catalan-Spanish and English-speaking children, scores of ≥ 75% and ≥ 70%, respectively, were highly likelihood to be children with normal language abilities.

CONCLUSIONS & IMPLICATIONS

The findings add to a pattern suggesting that SWL is a mechanism that children rely on to acquire vocabulary. The results also suggest that SWL deficits, in particular when combined with other measures, may be a reliable diagnostic indicator for children with DLD regardless of the child's native language, and whether or not the child is bilingual or monolingual.

WHAT THIS PAPER ADDS

What is already known on the subject Although there is some disagreement, a small but growing body of work suggests that deficits in procedural memory, as measured either by motor sequencing (Serial Reaction Time-SRT) or SWL tasks, may be part of the deficit profile of children with DLD. To date, studies have not examined SWL across linguistically heterogeneous populations of children with DLD to determine if it is a unique clinical marker of the disorder. What this paper adds to existing knowledge The results show that children with DLD, regardless of their native language, or whether the child is bi- or monolingual, have difficulties on SWL tasks, and that these deficits are linked to severity of the language disorder. Taken together, these results indicate that procedural memory deficits may be a core feature of DLD. This suggests that statistical-learning tasks using tone stimuli can also advance our understanding of statistical-learning abilities in children with DLD more globally. What are the potential or actual clinical implications of this work? The current study shows that statistical-learning tasks using tone stimuli can be used in conjunction with standardized assessment measures to differentiate children with DLD from children with typical language ability.

Revisiting the Role of the Medial Temporal Lobe in Motor Learning

Classic taxonomies of memory distinguish explicit and implicit memory systems, placing motor skills squarely in the latter branch. This assertion is in part a consequence of foundational discoveries showing significant motor learning in amnesics. Those findings suggest that declarative memory processes in the medial temporal lobe (MTL) do not contribute to motor learning. Here, we revisit this issue, testing an individual (L. S. J.) with severe MTL damage on four motor learning tasks and comparing her performance to age-matched controls. Consistent with previous findings in amnesics, we observed that L. S. J. could improve motor performance despite having significantly impaired declarative memory. However, she tended to perform poorly relative to age-matched controls, with deficits apparently related to flexible action selection. Further supporting an action selection deficit, L. S. J. fully failed to learn a task that required the acquisition of arbitrary action-outcome associations. We thus propose a modest revision to the classic taxonomic model: Although MTL-dependent memory processes are not necessary for some motor learning to occur, they play a significant role in the acquisition, implementation, and retrieval of action selection strategies. These findings have implications for our understanding of the neural correlates of motor learning, the psychological mechanisms of skill, and the theory of multiple memory systems.

Spatial and Temporal Context Jointly Modulate the Sensory Response within the Ventral Visual Stream

Both spatial and temporal context play an important role in visual perception and behavior. Humans can extract statistical regularities from both forms of context to help process the present and to construct expectations about the future. Numerous studies have found reduced neural responses to expected stimuli compared with unexpected stimuli, for both spatial and temporal regularities. However, it is largely unclear whether and how these forms of context interact. In the current fMRI study, 33 human volunteers were exposed to pairs of object stimuli that could be expected or surprising in terms of their spatial and temporal context. We found reliable independent contributions of both spatial and temporal context in modulating the neural response. Specifically, neural responses to stimuli in expected compared with unexpected contexts were suppressed throughout the ventral visual stream. These results suggest that both spatial and temporal context may aid sensory processing in a similar fashion, providing evidence on how different types of context jointly modulate perceptual processing.

A Process-Oriented View of Procedural Memory Can Help Better Understand Tourette's Syndrome

Tourette's syndrome (TS) is a neurodevelopmental disorder characterized by repetitive movements and vocalizations, also known as tics. The phenomenology of tics and the underlying neurobiology of the disorder have suggested that the altered functioning of the procedural memory system might contribute to its etiology. However, contrary to the robust findings of impaired procedural memory in neurodevelopmental disorders of language, results from TS have been somewhat mixed. We review the previous studies in the field and note that they have reported normal, impaired, and even enhanced procedural performance. These mixed findings may be at least partially be explained by the diversity of the samples in both age and tic severity, the vast array of tasks used, the low sample sizes, and the possible confounding effects of other cognitive functions, such as executive functions, working memory or attention. However, we propose that another often overlooked factor could also contribute to the mixed findings, namely the multiprocess nature of the procedural system itself. We propose that a process-oriented view of procedural memory functions could serve as a theoretical framework to help integrate these varied findings. We discuss evidence suggesting heterogeneity in the neural regions and their functional contributions to procedural memory. Our process-oriented framework can help to deepen our understanding of the complex profile of procedural functioning in TS and atypical development in general.

Neural processes underlying statistical learning for speech segmentation in dogs

To learn words, humans extract statistical regularities from speech. Multiple species use statistical learning also to process speech, but the neural underpinnings of speech segmentation in non-humans remain largely unknown. Here, we investigated computational and neural markers of speech segmentation in dogs, a phylogenetically distant mammal that efficiently navigates humans' social and linguistic environment. Using electroencephalography (EEG), we compared event-related responses (ERPs) for artificial words previously presented in a continuous speech stream with different distributional statistics. Results revealed an early effect (220-470 ms) of transitional probability and a late component (590-790 ms) modulated by both word frequency and transitional probability. Using fMRI, we searched for brain regions sensitive to statistical regularities in speech. Structured speech elicited lower activity in the basal ganglia, a region involved in sequence learning, and repetition enhancement in the auditory cortex. Speech segmentation in dogs, similar to that of humans, involves complex computations, engaging both domain-general and modality-specific brain areas. VIDEO ABSTRACT.

Evidence of hippocampal learning in human infants

The hippocampus is essential for human memory.¹ The protracted maturation of memory capacities from infancy through early childhood^2-4 is thus often attributed to hippocampal immaturity.^5-7 The hippocampus of human infants has been characterized in terms of anatomy,^8,9 but its function has never been tested directly because of technical challenges.^10,11 Here, we use recently developed methods for task-based fMRI in awake human infants¹² to test the hypothesis that the infant hippocampus supports statistical learning.^13-15 Hippocampal activity increased with exposure to visual sequences of objects when the temporal order contained regularities to be learned, compared to when the order was random. Despite the hippocampus doubling in anatomical volume across infancy, learning-related functional activity bore no relationship to age. This suggests that the hippocampus is recruited for statistical learning at the youngest ages in our sample, around 3 months. Within the hippocampus, statistical learning was clearer in anterior than posterior divisions. This is consistent with the theory that statistical learning occurs in the monosynaptic pathway,¹⁶ which is more strongly represented in the anterior hippocampus.^17,18 The monosynaptic pathway develops earlier than the trisynaptic pathway, which is linked to episodic memory,^19,20 raising the possibility that the infant hippocampus participates in statistical learning before it forms durable memories. Beyond the hippocampus, the medial prefrontal cortex showed statistical learning, consistent with its role in adult memory integration²¹ and generalization.²² These results suggest that the hippocampus supports the vital ability of infants to extract the structure of their environment through experience.