The context of uncertainty modulates the subcortical response to predictability

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.

Involvement of the cerebellum in the serial reaction time task (SRT) (Response to Janacsek et al.)

An ALE meta-analysis focused on the serial reaction time task published in NeuroImage (Janacsek et al., 2019) demonstrated consistent activation of the basal ganglia across neuroimaging studies featuring sequence ​> ​random block contrasts and no consistent cerebellar activation. To enable valid conclusions regarding the role of the cerebellum in this context, some of the included studies should be excluded (e.g., because the cerebellum was explicitly not scanned). After omitting 6 of 16 studies/subject groups, 70% of the remaining studies did report cerebellar activation. While an ALE analysis of the remaining contrasts confirmed the original results, it may lack the power to detect cerebellar effects. We argue the conclusion that the cerebellum is not involved in sequence-specific learning should be treated with caution.

Sequence learning in the human brain: A functional neuroanatomical meta-analysis of serial reaction time studies

Sequence learning underlies numerous motor, cognitive, and social skills. Previous models and empirical investigations of sequence learning in humans and non-human animals have implicated cortico-basal ganglia-cerebellar circuitry as well as other structures. To systematically examine the functional neuroanatomy of sequence learning in humans, we conducted a series of neuroanatomical meta-analyses. We focused on the serial reaction time (SRT) task. This task, which is the most widely used paradigm for probing sequence learning in humans, allows for the rigorous control of visual, motor, and other factors. Controlling for these factors (in sequence-random block contrasts), sequence learning yielded consistent activation only in the basal ganglia, across the striatum (anterior/mid caudate nucleus and putamen) and the globus pallidus. In contrast, when visual, motor, and other factors were not controlled for (in a global analysis with all sequence-baseline contrasts, not just sequence-random contrasts), premotor cortical and cerebellar activation were additionally observed. The study provides solid evidence that, at least as tested with the visuo-motor SRT task, sequence learning in humans relies on the basal ganglia, whereas cerebellar and premotor regions appear to contribute to aspects of the task not related to sequence learning itself. The findings have both basic research and translational implications.

Understanding the Neural Bases of Implicit and Statistical Learning

Both implicit learning and statistical learning focus on the ability of learners to pick up on patterns in the environment. It has been suggested that these two lines of research may be combined into a single construct of "implicit statistical learning." However, by comparing the neural processes that give rise to implicit versus statistical learning, we may determine the extent to which these two learning paradigms do indeed describe the same core mechanisms. In this review, we describe current knowledge about neural mechanisms underlying both implicit learning and statistical learning, highlighting converging findings between these two literatures. A common thread across all paradigms is that learning is supported by interactions between the declarative and nondeclarative memory systems of the brain. We conclude by discussing several outstanding research questions and future directions for each of these two research fields. Moving forward, we suggest that the two literatures may interface by defining learning according to experimental paradigm, with "implicit learning" reserved as a specific term to denote learning without awareness, which may potentially occur across all paradigms. By continuing to align these two strands of research, we will be in a better position to characterize the neural bases of both implicit and statistical learning, ultimately improving our understanding of core mechanisms that underlie a wide variety of human cognitive abilities.

Monitoring the past and choosing the future: the prefrontal cortical influences on voluntary action

Choosing between equivalent response options requires the resolution of ambiguity. One could facilitate such decisions by monitoring previous actions and implementing transient or arbitrary rules to differentiate response options. This would reduce the entropy of chosen actions. We examined voluntary action decisions during magnetoencephalography, identifying the spatiotemporal correlates of stimulus- and choice-entropy. Negative correlations between frontotemporal activity and entropy of past trials were observed after participants’ responses, reflecting sequential monitoring of recent events. In contrast, choice entropy correlated negatively with prefrontal activity, before and after participants’ response, consistent with transient activation of latent response-sets ahead of a decision and updating the monitor of recent decisions after responding. Individual differences in current choices were related to the strength of the prefrontal signals that reflect monitoring of the statistical regularities in previous events. Together, these results explain individual expressions of voluntary action, through differential engagement of prefrontal areas to guide sequential decisions.

Spatiotemporal Neurodynamics Underlying Internally and Externally Driven Temporal Prediction: A High Spatial Resolution ERP Study

Temporal prediction (TP) is a flexible and dynamic cognitive ability. Depending on the internal or external nature of information exploited to generate TP, distinct cognitive and brain mechanisms are engaged with the same final goal of reducing uncertainty about the future. In this study, we investigated the specific brain mechanisms involved in internally and externally driven TP. To this end, we employed an experimental paradigm purposely designed to elicit and compare externally and internally driven TP and a combined approach based on the application of a distributed source reconstruction modeling on a high spatial resolution electrophysiological data array. Specific spatiotemporal ERP signatures were identified, with significant modulation of contingent negative variation and frontal late sustained positivity in external and internal TP contexts, respectively. These different electrophysiological patterns were supported by the engagement of distinct neural networks, including a left sensorimotor and a prefrontal circuit for externally and internally driven TP, respectively.

The neural signature of information regularity in temporally extended event sequences

Statistical regularities exist at different timescales in temporally unfolding event sequences. Recent studies have identified brain regions that are sensitive to the levels of regularity in sensory inputs, enabling the brain to construct a representation of environmental structure and adaptively generate actions or predictions. However, the temporal specificity of the statistical regularity to which the brain responds remains largely unknown. This uncertainty applies to the regularities of sensory inputs as well as instrumental actions. Here, we used fMRI to investigate the neural correlates of regularity in sequences of task events and action selections in a visuomotor choice task. We quantified timescale-dependent regularity measures by calculating Shannon's entropy and surprise from a sliding-window of consecutive task events and actions. Activity in the frontopolar cortex negatively correlated with the entropy in action selection, while activity in the temporoparietal junction, the striatum, and the cerebellum negatively correlated with the entropy in stimulus events at longer timescales. In contrast, activity in the supplementary motor area, the superior frontal gyrus, and the superior parietal lobule was positively correlated with the surprise of each stimulus across different timescales. The results suggest a spatial distribution of regions sensitive to various information regularities according to a temporal hierarchy, which may play a central role in concurrently monitoring the regularity in previous and current events over different timescales to optimize behavioral control in a dynamic environment.

Neurocognitive mechanisms of statistical-sequential learning: what do event-related potentials tell us?

Statistical-sequential learning (SL) is the ability to process patterns of environmental stimuli, such as spoken language, music, or one's motor actions, that unfold in time. The underlying neurocognitive mechanisms of SL and the associated cognitive representations are still not well understood as reflected by the heterogeneity of the reviewed cognitive models. The purpose of this review is: (1) to provide a general overview of the primary models and theories of SL, (2) to describe the empirical research - with a focus on the event-related potential (ERP) literature - in support of these models while also highlighting the current limitations of this research, and (3) to present a set of new lines of ERP research to overcome these limitations. The review is articulated around three descriptive dimensions in relation to SL: the level of abstractness of the representations learned through SL, the effect of the level of attention and consciousness on SL, and the developmental trajectory of SL across the life-span. We conclude with a new tentative model that takes into account these three dimensions and also point to several promising new lines of SL research.

Dynamic cortical involvement in implicit anticipation during statistical learning

The prediction of future events is fundamental in a large number of critical neurobehavioral contexts including implicit motor learning. This learning process relies on the probabilities with which events occur, and is a dynamic phenomenon. The aim of present study was to investigate the development of anticipatory processes during implicit learning. A decision making task was employed in which the frequency of trial types was manipulated such that one trial type was disproportionately prevalent as compared to the remaining three trial types. A 275 channel whole-head magnetoencephalography (MEG) system was used to investigate the spatiotemporal distribution of event-related desynchronization (ERD) and synchronization (ERS). The results revealed that oscillations within the alpha (10-12 Hz) and beta (14-30 Hz) frequencies were associated with anticipatory processes in distinct networks in the course of learning. During early phases of learning the contralateral motor cortex, the anterior cingulate, the caudate and the inferior frontal gyrus showed ERDs within beta and alpha frequencies, putatively reflecting preparation of next motor response. As the task progressed, alpha ERSs in occipitotemporal regions and putamen likely reflect perceptual anticipation of the forthcoming stimuli.

Repetitive transcranial magnetic stimulation of the left dorsolateral prefrontal cortex improves probabilistic category learning

Traditionally, it has been thought that probabilistic category learning, one of the implicit memory functions, is dependent on the basal ganglia. However, there is growing evidence indicating the involvement of the dorsolateral prefrontal cortex (DLPFC) in probabilistic category learning. In order to identify the causal role of DLPFC in probabilistic category learning, we investigated whether repetitive transcranial magnetic stimulation (rTMS) over the left DLPFC influences the learning ability in healthy subjects using the weather prediction task (WPT). Application of 10 Hz rTMS over the left DLPFC induced significant improvement in the total hit rate during the total trials of the WPT, compared with sham stimulation. Specifically, the improvement was significant in the early and late learning blocks of the WPT, but not in the intermediate block of learning. These results indicate that the left DLPFC may play an important role in probabilistic category learning, possibly by influencing not only on embodied information application in late stage of the learning but also on memory encoding for working memory demands in early stage of the learning via frontostriatal and frontohippocampal circuits, respectively. They also may lend support to the possibility that rTMS can improve implicit learning ability in patients with basal ganglia disorders.

The neural substrate and functional integration of uncertainty in decision making: an information theory approach

Decision making can be regarded as the outcome of cognitive processes leading to the selection of a course of action among several alternatives. Borrowing a central measurement from information theory, Shannon entropy, we quantified the uncertainties produced by decisions of participants within an economic decision task under different configurations of reward probability and time. These descriptors were used to obtain blood oxygen level-dependent (BOLD) signal correlates of uncertainty and two clusters codifying the Shannon entropy of task configurations were identified: a large cluster including parts of the right middle cingulate cortex (MCC) and left and right pre-supplementary motor areas (pre-SMA) and a small cluster at the left anterior thalamus. Subsequent functional connectivity analyses using the psycho-physiological interactions model identified areas involved in the functional integration of uncertainty. Results indicate that clusters mostly located at frontal and temporal cortices experienced an increased connectivity with the right MCC and left and right pre-SMA as the uncertainty was higher. Furthermore, pre-SMA was also functionally connected to a rich set of areas, most of them associative areas located at occipital and parietal lobes. This study provides a map of the human brain segregation and integration (i.e., neural substrate and functional connectivity respectively) of the uncertainty associated to an economic decision making paradigm.

Neural evidence of statistical learning: efficient detection of visual regularities without awareness

Our environment contains regularities distributed in space and time that can be detected by way of statistical learning. This unsupervised learning occurs without intent or awareness, but little is known about how it relates to other types of learning, how it affects perceptual processing, and how quickly it can occur. Here we use fMRI during statistical learning to explore these questions. Participants viewed statistically structured versus unstructured sequences of shapes while performing a task unrelated to the structure. Robust neural responses to statistical structure were observed, and these responses were notable in four ways: First, responses to structure were observed in the striatum and medial temporal lobe, suggesting that statistical learning may be related to other forms of associative learning and relational memory. Second, statistical regularities yielded greater activation in category-specific visual regions (object-selective lateral occipital cortex and word-selective ventral occipito-temporal cortex), demonstrating that these regions are sensitive to information distributed in time. Third, evidence of learning emerged early during familiarization, showing that statistical learning can operate very quickly and with little exposure. Finally, neural signatures of learning were dissociable from subsequent explicit familiarity, suggesting that learning can occur in the absence of awareness. Overall, our findings help elucidate the underlying nature of statistical learning.

Serial reaction time performance following right parietal lobe damage

The serial reaction time task (SRT) is used to assess implicit sequence learning. Neuroimaging studies implicate parietal involvement; however, the necessity of this area is unclear. We tested six unilateral right parietal patients and compared their performance to matched controls. Both groups showed similar levels of learning and explicit awareness. Two patients with the largest lesions extending into either frontal or cerebellar regions showed no learning. These data suggest that implicit sequence learning can occur despite damage to the right parietal lobe.

Neurocognitive basis of implicit learning of sequential structure and its relation to language processing

The ability to learn and exploit environmental regularities is important for many aspects of skill learning, of which language may be a prime example. Much of such learning proceeds in an implicit fashion, that is, it occurs unintentionally and automatically and results in knowledge that is difficult to verbalize explicitly. An important research goal is to ascertain the underlying neurocognitive mechanisms of implicit learning abilities and understand its contribution to perception, language, and cognition more generally. In this article, we review recent work that investigates the extent to which implicit learning of sequential structure is mediated by stimulus-specific versus domain-general learning mechanisms. Although much of previous implicit learning research has emphasized its domain-general aspect, here we highlight behavioral work suggesting a modality-specific locus. Even so, our data also reveal that individual variability in implicit sequence learning skill correlates with performance on a task requiring sensitivity to the sequential context of spoken language, suggesting that implicit sequence learning to some extent is domain-general. Taking into consideration this behavioral work, in conjunction with recent imaging studies, we argue that implicit sequence learning and language processing are both complex, dynamic processes that partially share the same underlying neurocognitive mechanisms, specifically those that rely on the encoding and representation of phonological sequences.

Cognitive slowing in Parkinson disease is accompanied by hypofunctioning of the striatum

Objective: To investigate whether cognitive slowing in Parkinson disease (PD) reflects disruption of the basal ganglia or dysfunction of the frontal lobe by excluding an influence of abnormal brain activity due to motor deficits.

Methods: We measured neuronal activity during a verbal mental-operation task with H215O PET. This task enabled us to evaluate brain activity change associated with an increase in the cognitive speed without an influence on motor deficits.

Results: As the speed of the verbal mental-operation task increased, healthy controls exhibited proportional increase in activities in the anterior striatum and medial premotor cortex, suggesting the involvement of the corticobasal ganglia circuit in normal performance of the task. By contrast, patients with PD lacked an increase in the striatal activity, whereas the medial premotor cortex showed a proportional increase.

Conclusions: Although the present study chose a liberal threshold and needs subsequent confirmation, the findings suggest that striatal disruption resulting in abnormal processing in the corticobasal ganglia circuit may contribute to cognitive slowing in Parkinson disease, as is the case in motor slowing.

The influence of rTMS over the right dorsolateral prefrontal cortex on top-down attentional processes

Repetitive Transcranial Magnetic Stimulation (rTMS) provides a unique opportunity to study causal relationships between activity in the dorsolateral prefrontal cortex (DLPFC) and executive functioning, by modulating brain activity in SHAM controlled designs. We devised a new Stroop task paradigm in which subjects must engage in both strategic and automatic attentional processes. In the current experiment, we manipulated subjects' expectancies for incongruent stimuli. Previous research demonstrated that when subjects have a high level of expectancy that a stimulus will be incongruent, they are able to strategically adjust the relative influence of word reading on color naming. The effect of high frequency (HF) rTMS on Stroop performance of 20 right-handed healthy female volunteers was tested using a double blind within subjects design by counterbalanced crossover sham (placebo) and active rTMS over the right DLPFC. Since mood remained unchanged after rTMS, the Stroop data could be evaluated independent of mood changes. Only in the high expectancy condition, we found a decreased response time to both congruent and incongruent trials on the Stroop task performance after HF rTMS. The SHAM placebo condition yielded no effects. We conclude that high frequency stimulation over the right DLPFC has an effect on top-down attentional processes by modulating the attentional set.

Imaging valuation models in human choice

To make a decision, a system must assign value to each of its available choices. In the human brain, one approach to studying valuation has used rewarding stimuli to map out brain responses by varying the dimension or importance of the rewards. However, theoretical models have taught us that value computations are complex, and so reward probes alone can give only partial information about neural responses related to valuation. In recent years, computationally principled models of value learning have been used in conjunction with noninvasive neuroimaging to tease out neural valuation responses related to reward-learning and decision-making. We restrict our review to the role of these models in a new generation of experiments that seeks to build on a now-large body of diverse reward-related brain responses. We show that the models and the measurements based on them point the way forward in two important directions: the valuation of time and the valuation of fictive experience.

Oculomotor and manual indexes of incidental and intentional spatial sequence learning during middle childhood and adolescence

The goal of this study was to examine incidental and intentional spatial sequence learning during middle childhood and adolescence. We tested four age groups (8-10 years, 11-13 years, 14-17 years, and young adults [18+ years]) on a serial reaction time task and used manual and oculomotor measures to examine incidental sequence learning. Participants were also administered a trial block in which they were explicitly instructed to learn a sequence. Replicating our previous study with adults, oculomotor anticipations and response times showed learning effects similar to those in the manual modality. There were few age-related differences in the sequence learning indexes during incidental learning, but intentional learning yielded differences on all indexes. Results indicate that the search for regularities and the ability to learn a sequence rapidly under incidental conditions are mature by 8 to 10 years of age. In contrast, the ability to learn a sequence intentionally, which requires cognitive resources and strategies, continues to develop through adolescence.

Characterization of event-related designs using BOLD and IRON fMRI

Despite many desirable characteristics, event-related (ER) stimulus designs for BOLD and IRON suffer from low detection power relative to block designs because the hemodynamic impulse response function (IRF) acts as a low-pass filter on neural activation to attenuate the size of differential responses to alternate stimuli. While the use of exogenous contrast agent (IRON technique) provides an alternative fMRI method in animal models to improve sensitivity and spatial localization, the inherently slower hemodynamic IRF causes IRON detection efficiency to decrease faster than BOLD efficiency as the interstimulus interval (ISI) is shortened. Using simulations based upon assumptions of stimulus-response linearity and experimental data obtained in awake, non-human primates, this study compared detection efficiencies for fixed, random and semi-random ISI distributions for BOLD and IRON techniques. A larger relative gain in detection efficiency at short ISI was obtained by randomized designs using IRON contrast relative to BOLD contrast due to the slower IRF of the IRON method. To quantify tradeoffs between detection efficiency and the predictability of stimulus presentation, the Shannon entropy was introduced as an objective measure of predictability. Small amounts of entropy can be traded for large gains in efficiency, particularly for the IRON method.

Altered neural substrates of cognitive control in childhood ADHD: evidence from functional magnetic resonance imaging

OBJECTIVE

The study compared the neural bases of two cognitive control operations, interference suppression and response inhibition, between children with and children without attention deficit hyperactivity disorder (ADHD).

METHOD

Ten children (7-11 years of age) with combined-type ADHD and 10 comparison subjects matched for age and gender underwent rapid event-related functional magnetic resonance imaging (fMRI) during performance of a modified flanker task. Functional maps were generated through group averaging and performance-based correlational analyses.

RESULTS

Interference suppression in ADHD subjects was characterized by reduced engagement of a frontal-striatal-temporal-parietal network that subserved healthy performance. In contrast, response inhibition performance relied upon different regions in the two groups, frontal-striatal in comparison subjects but right superior temporal in ADHD children.

CONCLUSIONS

Alteration in the neural basis of two cognitive control operations in childhood ADHD was characterized by distinct, rather than unitary, patterns of functional abnormality. Greater between-group overlap in the neural network activated for interference suppression than in response inhibition suggests that components of cognitive control are differentially sensitive to ADHD. The ADHD children's inability to activate the caudate nucleus constitutes a core abnormality in ADHD. Observed functional abnormalities did not result from prolonged stimulant exposure, since most children were medication naive.

The roles of the caudate nucleus in human classification learning

The caudate nucleus is commonly active when learning relationships between stimuli and responses or categories. Previous research has not differentiated between the contributions to learning in the caudate and its contributions to executive functions such as feedback processing. We used event-related functional magnetic resonance imaging while participants learned to categorize visual stimuli as predicting "rain" or "sun." In each trial, participants viewed a stimulus, indicated their prediction via a button press, and then received feedback. Conditions were defined on the bases of stimulus-outcome contingency (deterministic, probabilistic, and random) and feedback (negative and positive). A region of interest analysis was used to examine activity in the head of the caudate, body/tail of the caudate, and putamen. Activity associated with successful learning was localized in the body and tail of the caudate and putamen; this activity increased as the stimulus-outcome contingencies were learned. In contrast, activity in the head of the caudate and ventral striatum was associated most strongly with processing feedback and decreased across trials. The left superior frontal gyrus was more active for deterministic than probabilistic stimuli; conversely, extrastriate visual areas were more active for probabilistic than deterministic stimuli. Overall, hippocampal activity was associated with receiving positive feedback but not with correct classification. Successful learning correlated positively with activity in the body and tail of the caudate nucleus and negatively with activity in the hippocampus.

Evidence of Developmental Differences in Implicit Sequence Learning: An fMRI Study of Children and Adults

Prevailing theories of implicit or unaware learning propose a developmental invariance model, with implicit function maturing early in infancy or childhood despite prolonged improvements in explicit or intentional learning and memory systems across childhood. Neuroimaging studies of adult visuomotor sequence learning have associated fronto-striatal brain regions with implicit learning of spatial sequences. Given evidence of continued development in these brain regions during childhood, we compare implicit sequence learning in adults and 7- to 11-year-old children to examine potential developmental differences in the recruitment of fronto-striatal circuitry during implicit learning. Participants performed a standard serial reaction time task. Stimuli alternately followed a fixed 10-step sequence of locations or were presented in a pseudorandom order of locations. Adults outperformed children, achieving a significantly larger sequence learning effect and showing learning more quickly than children. Age-related differences in activity were observed in the premotor cortex, putamen, hippocampus, inferotemporal cortex, and parietal cortex. We observed differential recruitment of cortical and subcortical motor systems between groups, presumably reflecting age differences in motor response execution. Adults showed greater hippocampal activity for sequence trials, whereas children demonstrated greater signal during random trials. Activity in the right caudate correlated significantly with behavioral measures of implicit learning for both age groups, although adults showed greater signal change than children overall, as would be expected given developmental differences in sequence learning magnitude. These results challenge the idea of developmental invariance in implicit learning and instead support a view of parallel developments in implicit and explicit learning systems.

Probability detection mechanisms and motor learning

The automatic detection of patterns or regularities in the environment is central to certain forms of motor learning, which are largely procedural and implicit. The rules underlying the detection and use of probabilistic information in the perceptual-motor domain are largely unknown. We conducted two experiments involving a motor learning task with direct and crossed mapping of motor responses in which probabilities were present at the stimulus set level, the response set level, and at the level of stimulus-response (S-R) mapping. We manipulated only one level at a time, while controlling for the other two. The results show that probabilities were detected only when present at the S-R mapping and motor levels, but not at the perceptual one (experiment 1), unless the perceptual features have a dimensional overlap with the S-R mapping rule (experiment 2). The effects of probability detection were mostly facilitatory at the S-R mapping, both facilitatory and inhibitory at the perceptual level, and predominantly inhibitory at the response-set level. The facilitatory effects were based on learning the absolute frequencies first and transitional probabilities later (for the S-R mapping rule) or both types of information at the same time (for perceptual level), whereas the inhibitory effects were based on learning first the transitional probabilities. Our data suggest that both absolute frequencies and transitional probabilities are used in motor learning, but in different temporal orders, according to the probabilistic properties of the environment. The results support the idea that separate neural circuits may be involved in detecting absolute frequencies as compared to transitional probabilities.

The encoding of saccadic eye movements within human posterior parietal cortex

Over the last few years, several functionally distinct subregions of the posterior parietal cortex (PPC) have been shown to subserve oculomotor control. Since these areas seem to overlap with regions whose activation is related to attention, we used functional magnetic resonance imaging to compare the cerebral activation pattern evoked by eye movements with different attentional loads, i.e., oscillatory saccades with different frequencies, as well as predictable, and unpredictable saccades. Our results show activation in largely overlapping networks with differing strength of activity and symmetry of involved areas. Predictable saccades having the shortest saccadic latency led to the most pronounced cerebral activity both in terms of cortical areas involved and signal intensity. Predictable and unpredictable saccades were dominated by activation within the right hemisphere, whereas oscillatory saccades showing the longest saccadic latency were dominated by activation within the left hemisphere. In all tasks, the centers of gravity of activation occurred within the posterior part of the intraparietal sulcus (IPS), while the predictable saccades additionally activated its anterior part. The enhanced activity during the execution of predictable saccades was probably related to top-down processing and/or the preparation of the upcoming eye movement. The hemispheric difference could arise from a predominant role of the right PPC for shifting spatial attention and the left PPC for shifting temporal attention. The differential encoding of saccadic eye movements within IPS indicates that the PPC splits up into different functional modules related to the particular demands of a saccade.

Efficiency, power, and entropy in event-related fMRI with multiple trial types. Part II: design of experiments

The performance of an experimental design for functional magnetic resonance imaging (fMRI) can be characterized by its estimation efficiency, which is the ability to make an estimate of the hemodynamic response, its detection power, which is the ability to detect an activation, and its conditional entropy, which is a measure of the randomness of the design. In Liu and Frank [Neuroimage 21 (2004) 387-400], it is shown that there is a fundamental theoretical trade-off between estimation efficiency and detection power for experiments with multiple trial types and that there is an empirical relation between estimation efficiency and conditional entropy. This paper provides an intuitive interpretation of the theoretical results and examines the practical implications of these results for the optimal design of fMRI experiments with multiple trial types. The properties of block designs, permuted block designs, m-sequence designs, clustered m-sequence designs, and mixed designs are explored. It is shown that these designs nearly achieve the theoretically predicted performance and can be used in practice to obtain advantageous trade-offs among efficiency, power, and entropy.

Probability cuing of target location facilitates visual search implicitly in normal participants and patients with hemispatial neglect

We explored how variability in the probability of target locations affects visual search in normal individuals and in patients with hemispatial neglect, a deficit in attending to the contralesional side of space. Young and elderly normal participants responded faster when targets appeared in the more probable region than when targets appeared in the less probable region. Similarly, patients were sensitive to the distribution of targets, even in the neglected field. Although the attentional gradient that characterizes neglect was not eliminated, the response facilitation due to the probability distribution was proportionate to that of control participants and equal in magnitude across the neglected field. All participants exploited the uneven distribution of targets to enhance task performance without explicit instructions to do so or awareness of biases in their behavior. These results suggest that attentional orientation and sensitivity to external probabilities are possibly dissociable. An early sensory and a late motor mechanism are postulated as possibly being involved in the observed probability-matching behavior of participants.