Reconsidering the Imaging Evidence Used to Implicate Prediction Error as the Driving Force behind Learning.

In this paper, we review the evidence that learning is driven by signaling of Prediction Error [PE] by some neurons. We model associative learning in artificial neural networks using Hebbian (non-PE) learning algorithms to investigate whether the data used to implicate PE in learning can arise without actual PE computation. We conclude that the metabolic demands of synaptic change during Hebbian learning would produce a PE-correlated component in functional magnetic resonance imaging (fMRI), which suggests that the research used to imply PE in learning is currently inconclusive

Recent advances in functional neuroimaging analysis for cognitive neuroscience

Functional magnetic resonance imaging and electro-/magneto-encephalography are some of the main neuroimaging technologies used by cognitive neuroscientists to study how the brain works. However, the methods for analysing the rich spatial and temporal data they provide are constantly evolving, and these new methods in turn allow new scientific questions to be asked about the brain. In this brief review, we highlight a handful of recent analysis developments that promise to further advance our knowledge about the working of the brain. These include (1) multivariate approaches to decoding the content of brain activity, (2) time-varying approaches to characterising states of brain connectivity, (3) neurobiological modelling of neuroimaging data, and (4) standardisation and big data initiatives.Peer reviewe

The Hippocampal Film Editor: Sensitivity and Specificity to Event Boundaries in Continuous Experience.

The function of the human hippocampus is normally investigated by experimental manipulation of discrete events. Less is known about what triggers hippocampal activity during more naturalistic, continuous experience. We hypothesized that the hippocampus would be sensitive to the occurrence of event boundaries, that is, moments in time identified by observers as a transition between events. To address this, we analyzed functional MRI data from two groups: one (n = 253, 131 female) who viewed an 8.5 min film and another (n = 15, 6 female) who viewed a 120 min film. We observed a strong hippocampal response at boundaries defined by independent observers, which was modulated by boundary salience (the number of observers that identified each boundary). In the longer film, there were sufficient boundaries to show that this modulation remained after covarying out a large number of perceptual factors. This hypothesis-driven approach was complemented by a data-driven approach, in which we identified hippocampal events as moments in time with the strongest hippocampal activity. The correspondence between these hippocampal events and event boundaries was highly significant, revealing that the hippocampal response is not only sensitive, but also specific to event boundaries. We conclude that event boundaries play a key role in shaping hippocampal activity during encoding of naturalistic events.SIGNIFICANCE STATEMENT Recent years have seen the field of human neuroscience research transitioning from experiments with simple stimuli to the study of more complex and naturalistic experience. Nonetheless, our understanding of the function of many brain regions, such as the hippocampus, is based primarily on the study of brief, discrete events. As a result, we know little of what triggers hippocampal activity in real-life settings when we are exposed to a continuous stream of information. When does the hippocampus "decide" to respond during the encoding of naturalistic experience? We reveal here that hippocampal activity measured by fMRI during film watching is both sensitive and specific to event boundaries, identifying a potential mechanism whereby event boundaries shape experience by modulation of hippocampal activity

Assumptions behind scoring source and item memory impact on conclusions about memory: A reply to Kellen and Singmann's comment (2017).

In our recent article in the journal Cortex (Cooper, Greve, & Henson, 2017), we examined memory for source and item information using data from two different source monitoring paradigms and six different groups of participants. When comparing standard accuracy analysis and various Multinomial Processing Tree (MPT) models, we found that the type of analysis determined the extent to which item and/or source memory differences were found across groups (healthy young and older groups, an older group with mild memory problems, and individuals with hippocampal lesions). Our main point was methodological: that one could draw different conclusions (e.g., whether ageing or hippocampal lesions affect only source memory, or both source and item memory) depending on the analysis used

Little evidence for Fast Mapping (FM) in adults: A review and discussion.

Conventional memory theory proposes that the hippocampus is initially responsible for encoding new information, before this responsibility is gradually transferred to the neocortex. Therefore, a report in 2011 by Sharon et al. of hippocampal-independent learning in humans was notable. These authors reported normal learning of new object-name associations under a Fast Mapping (FM) procedure in adults with hippocampal damage, who were amnesic according to more conventional explicit memorisation procedures. FM is an incidental learning paradigm, inspired by vocabulary acquisition in children, which is hypothesised to allow rapid, cortical-based memory formation. In the years since the original report, there has been, understandably, a growing interest in adult FM, not only because of its theoretical importance, but also because of its potential to help rehabilitate individuals with memory problems. We review the FM literature in individuals with amnesia and in healthy adults, using both explicit and implicit memory measures. Contrary to other recent reviews, we conclude that the evidence for FM in adults is weak, and restraint is needed before assuming the phenomenon exists

Neural Differentiation of Incorrectly Predicted Memories.

Frequently experiencing an item in a specific context leads to the prediction that this item will occur when we encounter the same context in future. However, this prediction sometimes turns out to be incorrect, and recent behavioural research suggests that such “prediction errors” improve encoding of new information (Greve et al. 2017)

Forward models demonstrate that repetition suppression is best modelled by local neural scaling

Funder: This work was supported by British Academy postdoctoral fellowship and a Marie Curie fellowship (753441) to A.A., a Cambridge University international scholarship and IDB merit scholarship award to H.A., and Medical Research Council programme grant (SUAG/010 RG91365) to R.N.H.Abstract: Inferring neural mechanisms from functional magnetic resonance imaging (fMRI) is challenging because the fMRI signal integrates over millions of neurons. One approach is to compare computational models that map neural activity to fMRI responses, to see which best predicts fMRI data. We use this approach to compare four possible neural mechanisms of fMRI adaptation to repeated stimuli (scaling, sharpening, repulsive shifting and attractive shifting), acting across three domains (global, local and remote). Six features of fMRI repetition effects are identified, both univariate and multivariate, from two independent fMRI experiments. After searching over parameter values, only the local scaling model can simultaneously fit all data features from both experiments. Thus fMRI stimulus repetition effects are best captured by down-scaling neuronal tuning curves in proportion to the difference between the stimulus and neuronal preference. These results emphasise the importance of formal modelling for bridging neuronal and fMRI levels of investigation

Response to commentaries on our review of Fast Mapping in adults.

We thank all the commentators for their thoughts on our review of Fast Mapping (FM) in adults, where we questioned the evidence that FM is a distinct learning mechanism, and urged caution over the excitement generated by the original report of FM in adults with amnesia using the fast mapping paradigm (FMP) . While some commentators remain convinced that there is good evidence to support a FM process in adults, most reported a skepticism similar to ours. Here we respond to the main comments, and clarify some of the terms of debate

Identifying age-invariant and age-limited mechanisms for enhanced memory performance: Insights from self-referential processing in younger and older adults.

Self-referential processing has been identified as a possible tool for supporting effective encoding processes in the elderly population. However, the importance of self-reference per se, relative to the increase in meaningful elaboration normally associated with self-reference instructions, remains unclear. The present study sought to explore this issue further by examining self-referential encoding strategies that inherently involve more extensive stimulus elaboration: episodic autobiographical memory (AM) retrieval and semantic AM retrieval. These were compared with an analogous task involving retrieval of general semantic knowledge, as well as traditional binary self-referential and semantic encoding judgments. We found that both AM retrieval and general semantic retrieval at encoding resulted in substantial enhancements to recall and recognition memory of concrete nouns relative to binary encoding judgments across both age groups. Furthermore, older adults exhibited larger benefits from this additional elaboration than did younger adults, leading to elimination of age-related deficits in recognition memory. However, younger adults showed an additional boost to subsequent memory following episodic, relative to semantic, AM retrieval during free recall that was not exhibited by older adults. This may be because of greater demands on frontally mediated control processes and cognitive resources associated with the use of this strategy. Taken together, the results suggest that the mnemonic benefits associated with self-referential processing vary substantially depending on the specific nature of the encoding strategy, and suggest that, under certain conditions, semantic processing and self-referential processing are equally effective in mitigating age-related deficits in memory performance.This research was supported by the BBSRC [grant number BB/L02263X/1]. A.N.T. is supported by a Cambridge Commonwealth Trust scholarship, R.N.H. by the UK Medical Research Council programme grant MC-A060-5PR10, and J.S.S. by a James S. McDonnell Foundation Scholar award.This is the author accepted manuscript. The final version is available from the American Psychological Association via http://dx.doi.org/ http://dx.doi.org/10.1037/a003911

Adaptive cortical parcellations for source reconstructed EEG/MEG connectomes.

There is growing interest in the rich temporal and spectral properties of the functional connectome of the brain that are provided by Electro- and Magnetoencephalography (EEG/MEG). However, the problem of leakage between brain sources that arises when reconstructing brain activity from EEG/MEG recordings outside the head makes it difficult to distinguish true connections from spurious connections, even when connections are based on measures that ignore zero-lag dependencies. In particular, standard anatomical parcellations for potential cortical sources tend to over- or under-sample the real spatial resolution of EEG/MEG. By using information from cross-talk functions (CTFs) that objectively describe leakage for a given sensor configuration and distributed source reconstruction method, we introduce methods for optimising the number of parcels while simultaneously minimising the leakage between them. More specifically, we compare two image segmentation algorithms: 1) a split-and-merge (SaM) algorithm based on standard anatomical parcellations and 2) a region growing (RG) algorithm based on all the brain vertices with no prior parcellation. Interestingly, when applied to minimum-norm reconstructions for EEG/MEG configurations from real data, both algorithms yielded approximately 70 parcels despite their different starting points, suggesting that this reflects the resolution limit of this particular sensor configuration and reconstruction method. Importantly, when compared against standard anatomical parcellations, resolution matrices of adaptive parcellations showed notably higher sensitivity and distinguishability of parcels. Furthermore, extensive simulations of realistic networks revealed significant improvements in network reconstruction accuracies, particularly in reducing false leakage-induced connections. Adaptive parcellations therefore allow a more accurate reconstruction of functional EEG/MEG connectomes