Latent structure in random sequences drives neural learning toward a rational bias.

People generally fail to produce random sequences by overusing alternating patterns and avoiding repeating ones-the gambler's fallacy bias. We can explain the neural basis of this bias in terms of a biologically motivated neural model that learns from errors in predicting what will happen next. Through mere exposure to random sequences over time, the model naturally develops a representation that is biased toward alternation, because of its sensitivity to some surprisingly rich statistical structure that emerges in these random sequences. Furthermore, the model directly produces the best-fitting bias-gain parameter for an existing Bayesian model, by which we obtain an accurate fit to the human data in random sequence production. These results show that our seemingly irrational, biased view of randomness can be understood instead as the perfectly reasonable response of an effective learning mechanism to subtle statistical structure embedded in random sequences.

Using precise word timing information improves decoding accuracy in a multiband-accelerated multimodal reading experiment.

The blood-oxygen-level-dependent (BOLD) signal measured in functional magnetic resonance imaging (fMRI) experiments is generally regarded as sluggish and poorly suited for probing neural function at the rapid timescales involved in sentence comprehension. However, recent studies have shown the value of acquiring data with very short repetition times (TRs), not merely in terms of improvements in contrast to noise ratio (CNR) through averaging, but also in terms of additional fine-grained temporal information. Using multiband-accelerated fMRI, we achieved whole-brain scans at 3-mm resolution with a TR of just 500 ms at both 3T and 7T field strengths. By taking advantage of word timing information, we found that word decoding accuracy across two separate sets of scan sessions improved significantly, with better overall performance at 7T than at 3T. The effect of TR was also investigated; we found that substantial word timing information can be extracted using fast TRs, with diminishing benefits beyond TRs of 1000 ms.

Modeling Contextual Modulation of Memory Associations in the Hippocampus.

We present a computational model of how memories can be contextually acquired and recalled in the hippocampus. Our adaptive contextual memory model comprises the lateral entorhinal cortex (LEC), the dentate gyrus (DG) and areas CA3 and CA1 in the hippocampus, and assumes external inputs about context that originate in the prefrontal cortex (PFC). Specifically, we propose that there is a top-down bias on the excitability of cells in the DG of the hippocampus that recruits a sub-population of cells to differentiate contexts, independent of experienced stimuli, expanding the "pattern separation" role typically attributed to the DG. It has been demonstrated in rats that if PFC is inactivated, both acquisition and recall of memory associations are impaired. However, PFC inactivation during acquisition of one set of memory associations surprisingly leads to subsequent facilitation of the acquisition of a conflicting set of memory associations in the same context under normal PFC operation. We provide here the first computational and algorithmic account of how the absence or presence of the top-down contextual biases on the excitability of DG cells during different learning phases of these experiments explains these data. Our model simulates PFC inactivation as the loss of inhibitory control on DG, which leads to full or partial activation of DG cells related to conflicting memory associations previously acquired in different contexts. This causes context-inappropriate memory traces to become active in the CA3 recurrent network and thereby the output CA1 area within the hippocampus. We show that these incongruous memory patterns proactively interfere with and slow the acquisition of new memory associations. Further, we demonstrate that pattern completion within CA3 in response to a partial cue for the recall of previously acquired memories is also impaired by PFC inactivation for the same reason. Pre-training the model with interfering memories in contexts different from those used in the experiments, simulating a lifetime of experiences, was crucial to reproduce the rat behavioral data. Finally, we made several testable predictions based on the model that suggest future experiments to deepen our understanding of brain-wide memory processes.

Investigation of Biases and Compensatory Strategies Using a Probabilistic Variant of the Wisconsin Card Sorting Test.

The Wisconsin Card Sorting Test (WCST) evaluates a subject's ability to shift to a new pattern of behavior in response to the presentation of unexpected negative feedback. The present study introduces a novel version of the traditional WCST by integrating a probabilistic component into its traditional rule shifting to add uncertainty to the task, as well as the option to forage for information during any particular trial. These changes transformed a task that is trivial for neurotypical individuals into a challenging environment useful for evaluating biases and compensatory strategizing. Sixty subjects performed the probabilistic WCST at four uncertainty levels to determine the effect of uncertainty on subject performance and strategy. Results revealed that increasing the level of uncertainty during a run of trials correlated with a reduction in rational strategizing in favor of both random choice and information foraging, evoking biases and suboptimal strategies such as satisfaction of search, negativity bias, and probability matching.

Activation of Broca's area during the production of spoken and signed language: a combined cytoarchitectonic mapping and PET analysis.

Broca's area in the inferior frontal gyrus consists of two cytoarchitectonically defined regions-Brodmann areas (BA) 44 and 45. Combining probabilistic maps of these two areas with functional neuroimaging data obtained using PET, it is shown that BA45, not BA44, is activated by both speech and signing during the production of language narratives in bilingual subjects fluent from early childhood in both American Sign Language (ASL) and English when the generation of complex movements and sounds is taken into account. It is BA44, not BA45, that is activated by the generation of complex articulatory movements of oral/laryngeal or limb musculature. The same patterns of activation are found for oral language production in a group of English speaking monolingual subjects. These findings implicate BA45 as the part of Broca's area that is fundamental to the modality-independent aspects of language generation.

Complementary learning systems.

This paper reviews the fate of the central ideas behind the complementary learning systems (CLS) framework as originally articulated in McClelland, McNaughton, and O'Reilly (1995). This framework explains why the brain requires two differentially specialized learning and memory systems, and it nicely specifies their central properties (i.e., the hippocampus as a sparse, pattern-separated system for rapidly learning episodic memories, and the neocortex as a distributed, overlapping system for gradually integrating across episodes to extract latent semantic structure). We review the application of the CLS framework to a range of important topics, including the following: the basic neural processes of hippocampal memory encoding and recall, conjunctive encoding, human recognition memory, consolidation of initial hippocampal learning in cortex, dynamic modulation of encoding versus recall, and the synergistic interactions between hippocampus and neocortex. Overall, the CLS framework remains a vital theoretical force in the field, with the empirical data over the past 15 years generally confirming its key principles.

Hippocampal anatomy supports the use of context in object recognition: a computational model.

The human hippocampus receives distinct signals via the lateral entorhinal cortex, typically associated with object features, and the medial entorhinal cortex, associated with spatial or contextual information. The existence of these distinct types of information calls for some means by which they can be managed in an appropriate way, by integrating them or keeping them separate as required to improve recognition. We hypothesize that several anatomical features of the hippocampus, including differentiation in connectivity between the superior/inferior blades of DG and the distal/proximal regions of CA3 and CA1, work together to play this information managing role. We construct a set of neural network models with these features and compare their recognition performance when given noisy or partial versions of contexts and their associated objects. We found that the anterior and posterior regions of the hippocampus naturally require different ratios of object and context input for optimal performance, due to the greater number of objects versus contexts. Additionally, we found that having separate processing regions in DG significantly aided recognition in situations where object inputs were degraded. However, split processing in both DG and CA3 resulted in performance tradeoffs, though the actual hippocampus may have ways of mitigating such losses.

A bio-inspired kinematic controller for obstacle avoidance during reaching tasks with real robots.

This paper describes a redundant robot arm that is capable of learning to reach for targets in space in a self-organized fashion while avoiding obstacles. Self-generated movement commands that activate correlated visual, spatial and motor information are used to learn forward and inverse kinematic control models while moving in obstacle-free space using the Direction-to-Rotation Transform (DIRECT). Unlike prior DIRECT models, the learning process in this work was realized using an online Fuzzy ARTMAP learning algorithm. The DIRECT-based kinematic controller is fault tolerant and can handle a wide range of perturbations such as joint locking and the use of tools despite not having experienced them during learning. The DIRECT model was extended based on a novel reactive obstacle avoidance direction (DIRECT-ROAD) model to enable redundant robots to avoid obstacles in environments with simple obstacle configurations. However, certain configurations of obstacles in the environment prevented the robot from reaching the target with purely reactive obstacle avoidance. To address this complexity, a self-organized process of mental rehearsals of movements was modeled, inspired by human and animal experiments on reaching, to generate plans for movement execution using DIRECT-ROAD in complex environments. These mental rehearsals or plans are self-generated by using the Fuzzy ARTMAP algorithm to retrieve multiple solutions for reaching each target while accounting for all the obstacles in its environment. The key aspects of the proposed novel controller were illustrated first using simple examples. Experiments were then performed on real robot platforms to demonstrate successful obstacle avoidance during reaching tasks in real-world environments.

Parietal reach region encodes reach depth using retinal disparity and vergence angle signals.

Performing a visually guided reach requires the ability to perceive the egocentric distance of a target in three-dimensional space. Previous studies have shown that the parietal reach region (PRR) encodes the two-dimensional location of frontoparallel targets in an eye-centered reference frame. To investigate how a reach target is represented in three dimensions, we recorded the spiking activity of PRR neurons from two rhesus macaques trained to fixate and perform memory reaches to targets at different depths. Reach and fixation targets were configured to explore whether neural activity directly reflects egocentric distance as the amplitude of the required motor command, which is the absolute depth of the target, or rather the relative depth of the target with reference to fixation depth. We show that planning activity in PRR represents the depth of the reach target as a function of disparity and fixation depth, the spatial parameters important for encoding the depth of a reach goal in an eye centered reference frame. The strength of modulation by disparity is maintained across fixation depth. Fixation depth gain modulates disparity tuning while preserving the location of peak tuning features in PRR neurons. The results show that individual PRR neurons code depth with respect to the fixation point, that is, in eye centered coordinates. However, because the activity is gain modulated by vergence angle, the absolute depth can be decoded from the population activity.