Distributed representations of rule identity and rule order in human frontal cortex and striatum

Task structure tailors the geometry of neural representations in human lateral prefrontal cortex

How do human brains represent tasks of varying structure? The lateral prefrontal cortex (lPFC) flexibly represents task information. However, principles that shape lPFC representational geometry remain unsettled. We use fMRI and pattern analyses to reveal the structure of lPFC representational geometries as humans perform two distinct categorization tasks- one with flat, conjunctive categories and another with hierarchical, context-dependent categories. We show that lPFC encodes task-relevant information with task-tailored geometries of intermediate dimensionality. These geometries preferentially enhance the separability of task-relevant variables while encoding a subset in abstract form. Specifically, in the flat task, a global axis encodes response-relevant categories abstractly, while category-specific local geometries are high-dimensional. In the hierarchy task, a global axis abstractly encodes the higher-level context, while low-dimensional, context-specific local geometries compress irrelevant information and abstractly encode the relevant information. Comparing these task geometries exposes generalizable principles by which lPFC tailors representations to different tasks.

The effect of context and reason on the neural correlates of intentions

Many studies have identified networks in parietal and prefrontal cortex that are involved in intentional action. Yet, our understanding of the way these networks are involved in intentions is still very limited. In this study, we investigate two characteristics of these processes: context- and reason-dependence of the neural states associated with intentions. We ask whether these states depend on the context a person is in and the reasons they have for choosing an action. We used a combination of functional magnetic resonance imaging (fMRI) and multivariate decoding to directly assess the context- and reason-dependency of the neural states underlying intentions. We show that action intentions can be decoded from fMRI data based on a classifier trained in the same context and with the same reason, in line with previous decoding studies. Furthermore, we found that intentions can be decoded across different reasons for choosing an action. However, decoding across different contexts was not successful. We found anecdotal to moderate evidence against context-invariant information in all regions of interest and for all conditions but one. These results suggest that the neural states associated with intentions are modulated by the context of the action.

Probabilistic and Deductive Reasoning in the Human Brain

Reasoning is a process of inference from given premises to new conclusions. Deductive reasoning is truth-preserving and conclusions can only be either true or false. Probabilistic reasoning is based on degrees of belief and conclusions can be more or less likely. While deductive reasoning requires people to focus on the logical structure of the inference and ignore its content, probabilistic reasoning requires the retrieval of prior knowledge from memory. Recently, however, some researchers have denied that deductive reasoning is a faculty of the human mind. What looks like deductive inference might actually also be probabilistic inference, only with extreme probabilities. We tested this assumption in an fMRI experiment with two groups of participants: one group was instructed to reason deductively, the other received probabilistic instructions. They could freely choose between a binary and a graded response to each problem. The conditional probability and the logical validity of the inferences were systematically varied. Results show that prior knowledge was only used in the probabilistic reasoning group. These participants gave graded responses more often than those in the deductive reasoning group and their reasoning was accompanied by activations in the hippocampus. Participants in the deductive group mostly gave binary responses and their reasoning was accompanied by activations in the anterior cingulate cortex, inferior frontal cortex, and parietal regions. These findings show that (1) deductive and probabilistic reasoning rely on different neurocognitive processes, (2) people can suppress their prior knowledge to reason deductively, and (3) not all inferences can be reduced to probabilistic reasoning.

Neural mechanisms underlying rule selection based on response evaluation: a near-infrared spectroscopy study

The ability of humans to use rules for organizing action demands a high level of executive control. Situational complexity mediates rule selection, from the adoption of a given rule to the selection of complex rules to achieve an appropriate response. Several rules have been proposed to be superordinate to human behavior in a cognitive hierarchy and mediated by different brain regions. In the present study, using a novel rule-selection task based on pre-response evaluations that require several cognitive operations, we examined whether the task is mediated by a specific region of the prefrontal cortex using near-infrared spectroscopy. We showed that the selection of rules, including prior evaluation of a stimulus, activates broader areas of the prefrontal and premotor regions than response selection based on a given rule. The results are discussed in terms of hierarchical cognitive models, the functional specialization of multiple-cognitive operations in the prefrontal cortex, and their contribution to a novel cognitive task.

Global connectivity fingerprints predict the domain generality of multiple-demand regions

A set of distributed cognitive control networks are known to contribute to diverse cognitive demands, yet it is unclear how these networks gain this domain-general capacity. We hypothesized that this capacity is largely due to the particular organization of the human brain's intrinsic network architecture. Specifically, we tested the possibility that each brain region's domain generality is reflected in its level of global (hub-like) intrinsic connectivity as well as its particular global connectivity pattern ("connectivity fingerprint"). Consistent with prior work, we found that cognitive control networks exhibited domain generality as they represented diverse task context information covering sensory, motor response, and logic rule domains. Supporting our hypothesis, we found that the level of global intrinsic connectivity (estimated with resting-state functional magnetic resonance imaging [fMRI]) was correlated with domain generality during tasks. Further, using a novel information fingerprint mapping approach, we found that each cognitive control region's unique rule response profile("information fingerprint") could be predicted based on its unique intrinsic connectivity fingerprint and the information content in regions outside cognitive control networks. Together, these results suggest that the human brain's intrinsic network architecture supports its ability to represent diverse cognitive task information largely via the location of multiple-demand regions within the brain's global network organization.

Strategic complexity and cognitive skills affect brain response in interactive decision-making

Deciding the best action in social settings requires decision-makers to consider their and others’ preferences, since the outcome depends on the actions of both. Numerous empirical investigations have demonstrated variability of behavior across individuals in strategic situations. While prosocial, moral, and emotional factors have been intensively investigated to explain this diversity, neuro-cognitive determinants of strategic decision-making and their relation with intelligence remain mostly unknown. This study presents a new model of the process of strategic decision-making in repeated interactions, first providing a precise measure of the environment’s complexity, and then analyzing how this complexity affects subjects’ performance and neural response. The results confirm the theoretical predictions of the model. The frequency of deviations from optimal behavior is explained by a combination of higher complexity of the strategic environment and cognitive skills of the individuals. Brain response correlates with strategic complexity, but only in the subgroups with higher cognitive skills. Furthermore, neural effects were only observed in a fronto-parietal network typically involved in single-agent tasks (the Multiple Demand Network), thus suggesting that neural processes dealing with cognitively demanding individual tasks also have a central role in interactive decision-making. Our findings contribute to understanding how cognitive factors shape strategic decision-making and may provide the neural pathway of the reported association between strategic sophistication and fluid intelligence.

Compositionality in a Parallel Architecture for Language Processing

Compositionality has been a central concept in linguistics and philosophy for decades, and it is increasingly prominent in many other areas of cognitive science. Its status, however, remains contentious. Here, I reassess the nature and scope of the principle of compositionality (Partee, 1995) from the perspective of psycholinguistics and cognitive neuroscience. First, I review classic arguments for compositionality and conclude that they fail to establish compositionality as a property of human language. Next, I state a new competence argument, acknowledging the fact that any competent user of a language L can assign to most expressions in L at least one meaning which is a function only of the meanings of the expression's parts and of its syntactic structure. I then discuss selected results from cognitive neuroscience, indicating that the human brain possesses the processing capacities presupposed by the competence argument. Finally, I outline a language processing architecture consistent with the neuroscience results, where semantic representations may be generated by a syntax-driven stream and by an "asyntactic" processing stream, jointly or independently. Compositionality is viewed as a constraint on computation in the former stream only.

Hierarchical Representation of Multistep Tasks in Multiple-Demand and Default Mode Networks

Task episodes consist of sequences of steps that are performed to achieve a goal. We used fMRI to examine neural representation of task identity, component items, and sequential position, focusing on two major cortical systems—the multiple-demand (MD) and default mode networks (DMN). Human participants (20 males, 22 females) learned six tasks each consisting of four steps. Inside the scanner, participants were cued which task to perform and then sequentially identified the target item of each step in the correct order. Univariate time course analyses indicated that intra-episode progress was tracked by a tonically increasing global response, plus an increasing phasic step response specific to MD regions. Inter-episode boundaries evoked a widespread response at episode onset, plus a marked offset response specific to DMN regions. Representational similarity analysis (RSA) was used to examine representation of task identity and component steps. Both networks represented the content and position of individual steps, however the DMN preferentially represented task identity while the MD network preferentially represented step-level information. Thus, although both MD and DMN networks are sensitive to step-level and episode-level information in the context of hierarchical task performance, they exhibit dissociable profiles in terms of both temporal dynamics and representational content. The results suggest collaboration of multiple brain regions in control of multistep behavior, with MD regions particularly involved in processing the detail of individual steps, and DMN adding representation of broad task context.

SIGNIFICANCE STATEMENT Achieving one's goals requires knowing what to do and when. Tasks are typically hierarchical, with smaller steps nested within overarching goals. For effective, flexible behavior, the brain must represent both levels. We contrast response time courses and information content of two major cortical systems—the multiple-demand (MD) and default mode networks (DMN)—during multistep task episodes. Both networks are sensitive to step-level and episode-level information, but with dissociable profiles. Intra-episode progress is tracked by tonically increasing global responses, plus MD-specific increasing phasic step responses. Inter-episode boundaries evoke widespread responses at episode onset, plus DMN-specific offset responses. Both networks represent content and position of individual steps; however, the DMN and MD networks favor task identity and step-level information, respectively.

Prefrontal reinstatement of contextual task demand is predicted by separable hippocampal patterns

Goal-directed behavior requires the representation of a task-set that defines the task-relevance of stimuli and guides stimulus-action mappings. Past experience provides one source of knowledge about likely task demands in the present, with learning enabling future predictions about anticipated demands. We examine whether spatial contexts serve to cue retrieval of associated task demands (e.g., context A and B probabilistically cue retrieval of task demands X and Y, respectively), and the role of the hippocampus and dorsolateral prefrontal cortex (dlPFC) in mediating such retrieval. Using 3D virtual environments, we induce context-task demand probabilistic associations and find that learned associations affect goal-directed behavior. Concurrent fMRI data reveal that, upon entering a context, differences between hippocampal representations of contexts (i.e., neural pattern separability) predict proactive retrieval of the probabilistically dominant associated task demand, which is reinstated in dlPFC. These findings reveal how hippocampal-prefrontal interactions support memory-guided cognitive control and adaptive behavior.

Spatial contexts are often predictive of the tasks to be performed in them (e.g., a kitchen predicts cooking). Here the authors show that the retrieval of task demand when encountering a spatial context depends on hippocampal-prefrontal interactions.

Representational Organization of Novel Task Sets during Proactive Encoding

Recent multivariate analyses of brain data have boosted our understanding of the organizational principles that shape neural coding. However, most of this progress has focused on perceptual visual regions (Connolly et al., 2012), whereas far less is known about the organization of more abstract, action-oriented representations. In this study, we focused on humans' remarkable ability to turn novel instructions into actions. While previous research shows that instruction encoding is tightly linked to proactive activations in frontoparietal brain regions, little is known about the structure that orchestrates such anticipatory representation. We collected fMRI data while participants (both males and females) followed novel complex verbal rules that varied across control-related variables (integrating within/across stimuli dimensions, response complexity, target category) and reward expectations. Using representational similarity analysis (Kriegeskorte et al., 2008), we explored where in the brain these variables explained the organization of novel task encoding, and whether motivation modulated these representational spaces. Instruction representations in the lateral PFC were structured by the three control-related variables, whereas intraparietal sulcus encoded response complexity and the fusiform gyrus and precuneus organized its activity according to the relevant stimulus category. Reward exerted a general effect, increasing the representational similarity among different instructions, which was robustly correlated with behavioral improvements. Overall, our results highlight the flexibility of proactive task encoding, governed by distinct representational organizations in specific brain regions. They also stress the variability of motivation-control interactions, which appear to be highly dependent on task attributes, such as complexity or novelty.SIGNIFICANCE STATEMENT In comparison with other primates, humans display a remarkable success in novel task contexts thanks to our ability to transform instructions into effective actions. This skill is associated with proactive task-set reconfigurations in frontoparietal cortices. It remains yet unknown, however, how the brain encodes in anticipation the flexible, rich repertoire of novel tasks that we can achieve. Here we explored cognitive control and motivation-related variables that might orchestrate the representational space for novel instructions. Our results showed that different dimensions become relevant for task prospective encoding, depending on the brain region, and that the lateral PFC simultaneously organized task representations following different control-related variables. Motivation exerted a general modulation upon this process, diminishing rather than increasing distances among instruction representations.

Encoding of Novel Verbal Instructions for Prospective Action in the Lateral Prefrontal Cortex: Evidence from Univariate and Multivariate Functional Magnetic Resonance Imaging Analysis

Verbal instructions are central to humans' capacity to learn new behaviors with minimal training, but the neurocognitive mechanisms involved in verbally instructed behaviors remain puzzling. Recent functional magnetic resonance imaging (fMRI) evidence suggests that the right middle frontal gyrus and dorsal premotor cortex (rMFG-dPMC) supports the translation of symbolic stimulus–response mappings into sensorimotor representations. Here, we set out to (1) replicate this finding, (2) investigate whether this region's involvement is specific to novel (vs. trained) instructions, and (3) study whether rMFG-dPMC also shows differences in its (voxel) pattern response indicative of general cognitive processes of instruction implementation. Participants were shown instructions, which they either had to perform later or merely memorize. Orthogonal to this manipulation, the instructions were either entirely novel or had been trained before the fMRI session. Results replicate higher rMFG-dPMC activation levels during instruction implementation versus memorization and show how this difference is restricted to novel, but not trained, instruction presentations. Pattern similarity analyses at the voxel level further reveal more consistent neural pattern responses in rMFG-dPMC during the implementation of novel versus trained instructions. In fact, this more consistent neural pattern response seemed to be specific to the first instruction presentation and disappeared after the instruction had been applied once. These results further support a role of rMFG-dPMC in the implementation of novel task instructions and highlight potentially important differences in studying this region's gross activation levels versus (the consistency of) its response patterns.

Just above Chance: Is It Harder to Decode Information from Prefrontal Cortex Hemodynamic Activity Patterns?

The prefrontal cortex (PFC) is central to flexible, goal-directed cognition, and understanding its representational code is an important problem in cognitive neuroscience. In humans, multivariate pattern analysis (MVPA) of fMRI blood oxygenation level-dependent (BOLD) measurements has emerged as an important approach for studying neural representations. Many previous studies have implicitly assumed that MVPA of fMRI BOLD is just as effective in decoding information encoded in PFC neural activity as it is in visual cortex. However, MVPA studies of PFC have had mixed success. Here we estimate the base rate of decoding information from PFC BOLD activity patterns from a meta-analysis of published MVPA studies. We show that PFC has a significantly lower base rate (55.4%) than visual areas in occipital (66.6%) and temporal (71.0%) cortices and one that is close to chance levels. Our results have implications for the design and interpretation of MVPA studies of PFC and raise important questions about its functional organization.

Decoding rule search domain in the left inferior frontal gyrus

Traditionally, the left hemisphere has been thought to extract mainly verbal patterns of information, but recent evidence has shown that the left Inferior Frontal Gyrus (IFG) is active during inductive reasoning in both the verbal and spatial domains. We aimed to understand whether the left IFG supports inductive reasoning in a domain-specific or domain-general fashion. To do this we used Multi-Voxel Pattern Analysis to decode the representation of domain during a rule search task. Thirteen participants were asked to extract the rule underlying streams of letters presented in different spatial locations. Each rule was either verbal (letters forming words) or spatial (positions forming geometric figures). Our results show that domain was decodable in the left prefrontal cortex, suggesting that this region represents domain-specific information, rather than processes common to the two domains. A replication study with the same participants tested two years later confirmed these findings, though the individual representations changed, providing evidence for the flexible nature of representations. This study extends our knowledge on the neural basis of goal-directed behaviors and on how information relevant for rule extraction is flexibly mapped in the prefrontal cortex.

The same analysis approach: Practical protection against the pitfalls of novel neuroimaging analysis methods

Standard neuroimaging data analysis based on traditional principles of experimental design, modelling, and statistical inference is increasingly complemented by novel analysis methods, driven e.g. by machine learning methods. While these novel approaches provide new insights into neuroimaging data, they often have unexpected properties, generating a growing literature on possible pitfalls. We propose to meet this challenge by adopting a habit of systematic testing of experimental design, analysis procedures, and statistical inference. Specifically, we suggest to apply the analysis method used for experimental data also to aspects of the experimental design, simulated confounds, simulated null data, and control data. We stress the importance of keeping the analysis method the same in main and test analyses, because only this way possible confounds and unexpected properties can be reliably detected and avoided. We describe and discuss this Same Analysis Approach in detail, and demonstrate it in two worked examples using multivariate decoding. With these examples, we reveal two sources of error: A mismatch between counterbalancing (crossover designs) and cross-validation which leads to systematic below-chance accuracies, and linear decoding of a nonlinear effect, a difference in variance.

Neural Representations of Hierarchical Rule Sets: The Human Control System Represents Rules Irrespective of the Hierarchical Level to Which They Belong

Humans use rules to organize their actions to achieve specific goals. Although simple rules that link a sensory stimulus to one response may suffice in some situations, often, the application of multiple, hierarchically organized rules is required. Recent theories suggest that progressively higher level rules are encoded along an anterior-to-posterior gradient within PFC. Although some evidence supports the existence of such a functional gradient, other studies argue for a lesser degree of specialization within PFC. We used fMRI to investigate whether rules at different hierarchical levels are represented at distinct locations in the brain or encoded by a single system. Thirty-seven male and female participants represented and applied hierarchical rule sets containing one lower-level stimulus-response rule and one higher-level selection rule. We used multivariate pattern analysis to investigate directly the representation of rules at each hierarchical level in absence of information about rules from other levels or other task-related information, thus providing a clear identification of low- and high-level rule representations. We could decode low- and high-level rules from local patterns of brain activity within a wide frontoparietal network. However, no significant difference existed between regions encoding representations of rules from both levels except for precentral gyrus, which represented only low-level rule information. Our findings show that the brain represents conditional rules regardless of their level in the explored hierarchy, so the human control system did not organize task representation according to this dimension. Our paradigm represents a promising approach to identifying critical principles that shape this control system.SIGNIFICANCE STATEMENT Several recent studies investigating the organization of the human control system propose that rules at different control levels are organized along an anterior-to-posterior gradient within PFC. In this study, we used multivariate pattern analysis to explore independently the representation of formally identical conditional rules belonging to different levels of a cognitive hierarchy and provide for the first time a clear identification of low- and high-level rule representations. We found no major spatial differences between regions encoding rules from different hierarchical levels. This suggests that the human brain does not use levels in the investigated hierarchy as a topographical organization principle to represent rules controlling our behavior. Our paradigm represents a promising approach to identifying which principles are critical.

Switch-Independent Task Representations in Frontal and Parietal Cortex

Alternating between two tasks is effortful and impairs performance. Previous fMRI studies have found increased activity in frontoparietal cortex when task switching is required. One possibility is that the additional control demands for switch trials are met by strengthening task representations in the human brain. Alternatively, on switch trials, the residual representation of the previous task might impede the buildup of a neural task representation. This would predict weaker task representations on switch trials, thus also explaining the performance costs. To test this, male and female participants were cued to perform one of two similar tasks, with the task being repeated or switched between successive trials. Multivoxel pattern analysis was used to test which regions encode the tasks and whether this encoding differs between switch and repeat trials. As expected, we found information about task representations in frontal and parietal cortex, but there was no difference in the decoding accuracy of task-related information between switch and repeat trials. Using cross-classification, we found that the frontoparietal cortex encodes tasks using a generalizable spatial pattern in switch and repeat trials. Therefore, task representations in frontal and parietal cortex are largely switch independent. We found no evidence that neural information about task representations in these regions can explain behavioral costs usually associated with task switching.SIGNIFICANCE STATEMENT Alternating between two tasks is effortful and slows down performance. One possible explanation is that the representations in the human brain need time to build up and are thus weaker on switch trials, explaining performance costs. Alternatively, task representations might even be enhanced to overcome the previous task. Here, we used a combination of fMRI and a brain classifier to test whether the additional control demands under switching conditions lead to an increased or decreased strength of task representations in frontoparietal brain regions. We found that task representations are not modulated significantly by switching processes and generalize across switching conditions. Therefore, task representations in the human brain cannot account for the performance costs associated with alternating between tasks.

Rule Encoding in Orbitofrontal Cortex and Striatum Guides Selection

Active maintenance of rules, like other executive functions, is often thought to be the domain of a discrete executive system. An alternative view is that rule maintenance is a broadly distributed function relying on widespread cortical and subcortical circuits. Tentative evidence supporting this view comes from research showing some rule selectivity in the orbitofrontal cortex and dorsal striatum. We recorded in these regions and in the ventral striatum, which has not been associated previously with rule representation, as macaques performed a Wisconsin Card Sorting Task. We found robust encoding of rule category (color vs shape) and rule identity (six possible rules) in all three regions. Rule identity modulated responses to potential choice targets, suggesting that rule information guides behavior by highlighting choice targets. The effects that we observed were not explained by differences in behavioral performance across rules and thus cannot be attributed to reward expectation. Our results suggest that rule maintenance and rule-guided selection of options are distributed processes and provide new insight into orbital and striatal contributions to executive control.

SIGNIFICANCE STATEMENT

Rule maintenance, an important executive function, is generally thought to rely on dorsolateral brain regions. In this study, we examined activity of single neurons in orbitofrontal cortex and in ventral and dorsal striatum of macaques in a Wisconsin Card Sorting Task. Neurons in all three areas encoded rules and rule categories robustly. Rule identity also affected neural responses to potential choice options, suggesting that stored information is used to influence decisions. These results endorse the hypothesis that rule maintenance is a broadly distributed mental operation.

Fluid Intelligence Predicts Novel Rule Implementation in a Distributed Frontoparietal Control Network

Fluid intelligence has been associated with a distributed cognitive control or multiple-demand (MD) network, comprising regions of lateral frontal, insular, dorsomedial frontal, and parietal cortex. Human fluid intelligence is also intimately linked to task complexity, and the process of solving complex problems in a sequence of simpler, more focused parts. Here, a complex target detection task included multiple independent rules, applied one at a time in successive task epochs. Although only one rule was applied at a time, increasing task complexity (i.e., the number of rules) impaired performance in participants of lower fluid intelligence. Accompanying this loss of performance was reduced response to rule-critical events across the distributed MD network. The results link fluid intelligence and MD function to a process of attentional focus on the successive parts of complex behavior.SIGNIFICANCE STATEMENT Fluid intelligence is intimately linked to the ability to structure complex problems in a sequence of simpler, more focused parts. We examine the basis for this link in the functions of a distributed frontoparietal or multiple-demand (MD) network. With increased task complexity, participants of lower fluid intelligence showed reduced responses to task-critical events. Reduced responses in the MD system were accompanied by impaired behavioral performance. Low fluid intelligence is linked to poor foregrounding of task-critical information across a distributed MD system.

Parsing the Roles of the Frontal Lobes and Basal Ganglia in Task Control Using Multivoxel Pattern Analysis

Cognitive control has traditionally been associated with pFC based on observations of deficits in patients with frontal lesions. However, evidence from patients with Parkinson disease indicates that subcortical regions also contribute to control under certain conditions. We scanned 17 healthy volunteers while they performed a task-switching paradigm that previously dissociated performance deficits arising from frontal lesions in comparison with Parkinson disease, as a function of the abstraction of the rules that are switched. From a multivoxel pattern analysis by Gaussian Process Classification, we then estimated the forward (generative) model to infer regional patterns of activity that predict Switch/Repeat behavior between rule conditions. At 1000 permutations, Switch/Repeat classification accuracy for concrete rules was significant in the BG, but at chance in the frontal lobe. The inverse pattern was obtained for abstract rules, whereby the conditions were successfully discriminated in the frontal lobe but not in the BG. This double dissociation highlights the difference between cortical and subcortical contributions to cognitive control and demonstrates the utility of multivariate approaches in investigations of functions that rely on distributed and overlapping neural substrates.

fMRI single trial discovery of spatio-temporal brain activity patterns

There is growing interest in the description of short-lived patterns in the spatiotemporal cortical activity monitored via neuroimaging. Most traditional analysis methods, designed to estimate relatively long-term brain dynamics, are not always appropriate to capture these patterns. Here we introduce a novel data-driven approach for detecting short-lived fMRI brain activity patterns. Exploiting Density Peak Clustering (Rodriguez and Laio [2014]), our approach reveals well localized clusters by identifying and grouping together voxels whose time-series are similar, irrespective of their brain location, even when very short time windows (∼10 volumes) are used. The method, which we call Coherence Density Peak Clustering (CDPC), is first tested on simulated data and compared with a standard unsupervised approach for fMRI analysis, independent component analysis (ICA). CDPC identifies activated voxels with essentially no false-positives and proves more reliable than ICA, which is troubled by a number of false positives comparable to that of true positives. The reliability of the method is demonstrated on real fMRI data from a simple motor task, containing brief iterations of the same movement. The clusters identified are found in regions expected to be involved in the task, and repeat synchronously with the paradigm. The methodology proposed is especially suitable for the study of short-time brain dynamics and single trial experiments, where the event or task of interest cannot be repeated for the same subject, as happens, for instance, in problem-solving, learning and decision-making. A GUI implementation of our method is available for download at https://github.com/micheleallegra/CDPC. Hum Brain Mapp 38:1421-1437, 2017. © 2016 Wiley Periodicals, Inc.

Dissociable roles of medial and lateral PFC in rule learning

INTRODUCTION

Although the neural basis of rule learning is of great interest to cognitive neuroscientists, the pattern of transient brain activation during rule discovery remains to be investigated.

METHOD

In this study, we measured event-related functional magnetic resonance imaging (fMRI) during distinct phases of rule learning. Twenty-one healthy human volunteers were presented with a series of cards, each containing a clock-like display of 12 circles numbered sequentially. Participants were instructed that a fictitious animal would move from one circle to another either in a regular pattern (according to a rule hidden in consecutive trials) or randomly. Participants were then asked to judge whether a given step followed a rule.

RESULTS

While the rule-search phase evoked more activation in the posterior lateral prefrontal cortex (LPFC), the rule-following phase caused stronger activation in the anterior medial prefrontal cortex (MPFC). Importantly, the intermediate phase, the rule-discovery phase evoked more activations in MPFC and dorsal anterior cingulate cortex (dACC) than rule search, and more activations in LPFC than rule following.

CONCLUSION

Therefore, we can conclude that the medial and lateral PFC have dissociable contributions in rule learning.

The Structural and Functional Organization of Cognition

This article proposes that what have been historically and contemporarily defined as different domains of human cognition are served by one of four functionally- and structurally-distinct areas of the prefrontal cortex (PFC). Their contributions to human intelligence are as follows: (a) BA9, enables our emotional intelligence, engaging the psychosocial domain; (b) BA47, enables our practical intelligence, engaging the material domain; (c) BA46 (or BA46-9/46), enables our abstract intelligence, engaging the hypothetical domain; and (d) BA10, enables our temporal intelligence, engaging in planning within any of the other three domains. Given their unique contribution to human cognition, it is proposed that these areas be called the, social (BA9), material (BA47), abstract (BA46-9/46) and temporal (BA10) mind. The evidence that BA47 participates strongly in verbal and gestural communication suggests that language evolved primarily as a consequence of the extreme selective pressure for practicality; an observation supported by the functional connectivity between BA47 and orbital areas that negatively reinforce lying. It is further proposed that the abstract mind (BA46-9/46) is the primary seat of metacognition charged with creating adaptive behavioral strategies by generating higher-order concepts (hypotheses) from lower-order concepts originating from the other three domains of cognition.

Coding of Visual, Auditory, Rule, and Response Information in the Brain: 10 Years of Multivoxel Pattern Analysis

How is the processing of task information organized in the brain? Many views of brain function emphasize modularity, with different regions specialized for processing different types of information. However, recent accounts also highlight flexibility, pointing especially to the highly consistent pattern of frontoparietal activation across many tasks. Although early insights from functional imaging were based on overall activation levels during different cognitive operations, in the last decade many researchers have used multivoxel pattern analyses to interrogate the representational content of activations, mapping out the brain regions that make particular stimulus, rule, or response distinctions. Here, we drew on 100 searchlight decoding analyses from 57 published papers to characterize the information coded in different brain networks. The outcome was highly structured. Visual, auditory, and motor networks predominantly (but not exclusively) coded visual, auditory, and motor information, respectively. By contrast, the frontoparietal multiple-demand network was characterized by domain generality, coding visual, auditory, motor, and rule information. The contribution of the default mode network and voxels elsewhere was minor. The data suggest a balanced picture of brain organization in which sensory and motor networks are relatively specialized for information in their own domain, whereas a specific frontoparietal network acts as a domain-general "core" with the capacity to code many different aspects of a task.

Multiple neural representations of elementary logical connectives

A defining trait of human cognition is the capacity to form compounds out of simple thoughts. This ability relies on the logical connectives AND, OR and IF. Simple propositions, e.g., 'There is a fork' and 'There is a knife', can be combined in alternative ways using logical connectives: e.g., 'There is a fork AND there is a knife', 'There is a fork OR there is a knife', 'IF there is a fork, there is a knife'. How does the brain represent compounds based on different logical connectives, and how are compounds evaluated in relation to new facts? In the present study, participants had to maintain and evaluate conjunctive (AND), disjunctive (OR) or conditional (IF) compounds while undergoing functional MRI. Our results suggest that, during maintenance, the left posterior inferior frontal gyrus (pIFG, BA44, or Broca's area) represents the surface form of compounds. During evaluation, the left pIFG switches to processing the full logical meaning of compounds, and two additional areas are recruited: the left anterior inferior frontal gyrus (aIFG, BA47) and the left intraparietal sulcus (IPS, BA40). The aIFG shows a pattern of activation similar to pIFG, and compatible with processing the full logical meaning of compounds, whereas activations in IPS differ with alternative interpretations of conditionals: logical vs conjunctive. These results uncover the functions of a basic cortical network underlying human compositional thought, and provide a shared neural foundation for the cognitive science of language and reasoning.

The Role of the Parietal Cortex in the Representation of Task-Reward Associations

Rewards obtained from specific behaviors can and do change across time. To adapt to such conditions, humans need to represent and update associations between behaviors and their outcomes. Much previous work focused on how rewards affect the processing of specific tasks. However, abstract associations between multiple potential behaviors and multiple rewards are an important basis for adaptation as well. In this experiment, we directly investigated which brain areas represent associations between multiple tasks and rewards, using time-resolved multivariate pattern analysis of functional magnetic resonance imaging data. Importantly, we were able to dissociate neural signals reflecting task-reward associations from those related to task preparation and reward expectation processes, variables that were often correlated in previous research. We hypothesized that brain regions involved in processing tasks and/or rewards will be involved in processing associations between them. Candidate areas included the dorsal anterior cingulate cortex, which is involved in associating simple actions and rewards, and the parietal cortex, which has been shown to represent task rules and action values. Our results indicate that local spatial activation patterns in the inferior parietal cortex indeed represent task-reward associations. Interestingly, the parietal cortex flexibly changes its content of representation within trials. It first represents task-reward associations, later switching to process tasks and rewards directly. These findings highlight the importance of the inferior parietal cortex in associating behaviors with their outcomes and further show that it can flexibly reconfigure its function within single trials. Significance statement: Rewards obtained from specific behaviors rarely remain constant over time. To adapt to changing conditions, humans need to continuously update and represent the current association between behavior and its outcomes. However, little is known about the neural representation of behavior-outcome associations. Here, we used multivariate pattern analysis of functional magnetic resonance imaging data to investigate the neural correlates of such associations. Our results demonstrate that the parietal cortex plays a central role in representing associations between multiple behaviors and their outcomes. They further highlight the flexibility of the parietal cortex, because we find it to adapt its function to changing task demands within trials on relatively short timescales.

Episodes, events, and models

We describe a novel computational theory of how individuals segment perceptual information into representations of events. The theory is inspired by recent findings in the cognitive science and cognitive neuroscience of event segmentation. In line with recent theories, it holds that online event segmentation is automatic, and that event segmentation yields mental simulations of events. But it posits two novel principles as well: first, discrete episodic markers track perceptual and conceptual changes, and can be retrieved to construct event models. Second, the process of retrieving and reconstructing those episodic markers is constrained and prioritized. We describe a computational implementation of the theory, as well as a robotic extension of the theory that demonstrates the processes of online event segmentation and event model construction. The theory is the first unified computational account of event segmentation and temporal inference. We conclude by demonstrating now neuroimaging data can constrain and inspire the construction of process-level theories of human reasoning.

Flexible Coding of Task Rules in Frontoparietal Cortex: An Adaptive System for Flexible Cognitive Control

How do our brains achieve the cognitive control that is required for flexible behavior? Several models of cognitive control propose a role for frontoparietal cortex in the structure and representation of task sets or rules. For behavior to be flexible, however, the system must also rapidly reorganize as mental focus changes. Here we used multivoxel pattern analysis of fMRI data to demonstrate adaptive reorganization of frontoparietal activity patterns following a change in the complexity of the task rules. When task rules were relatively simple, frontoparietal cortex did not hold detectable information about these rules. In contrast, when the rules were more complex, frontoparietal cortex showed clear and decodable rule discrimination. Our data demonstrate that frontoparietal activity adjusts to task complexity, with better discrimination of rules that are behaviorally more confusable. The change in coding was specific to the rule element of the task and was not mirrored in more specialized cortex (early visual cortex) where coding was independent of difficulty. In line with an adaptive view of frontoparietal function, the data suggest a system that rapidly reconfigures in accordance with the difficulty of a behavioral task. This system may provide a neural basis for the flexible control of human behavior.

Language control in bilinguals: Intention to speak vs. execution of speech

Bilinguals require a high degree of cognitive control to select the language intended for speaking and inhibit the unintended. Previous neuroimaging studies have not teased apart brain regions for generating the intention to use a given language, and those for speaking in that language. Separating these two phases can clarify at what stage competition between languages occurs. In this fMRI study German-English bilinguals were first cued to use German or English. After a delay, they named a picture in the cued language. During the intention phase, the precuneus, right superior lateral parietal lobule, and middle temporal gyrus were more activated when participants had to update the currently active language. During language execution activation was higher for English compared to German in brain areas associated with cognitive control, most notably the anterior cingulate and the caudate. Our results suggest two different systems enabling cognitive control during bilingual language production.

Medial prefrontal cortex predicts internally driven strategy shifts

Many daily behaviors require us to actively focus on the current task and ignore all other distractions. Yet, ignoring everything else might hinder the ability to discover new ways to achieve the same goal. Here, we studied the neural mechanisms that support the spontaneous change to better strategies while an established strategy is executed. Multivariate neuroimaging analyses showed that before the spontaneous change to an alternative strategy, medial prefrontal cortex (MPFC) encoded information that was irrelevant for the current strategy but necessary for the later strategy. Importantly, this neural effect was related to future behavioral changes: information encoding in MPFC was changed only in participants who eventually switched their strategy and started before the actual strategy change. This allowed us to predict spontaneous strategy shifts ahead of time. These findings suggest that MPFC might internally simulate alternative strategies and shed new light on the organization of PFC.

The Decoding Toolbox (TDT): a versatile software package for multivariate analyses of functional imaging data

The multivariate analysis of brain signals has recently sparked a great amount of interest, yet accessible and versatile tools to carry out decoding analyses are scarce. Here we introduce The Decoding Toolbox (TDT) which represents a user-friendly, powerful and flexible package for multivariate analysis of functional brain imaging data. TDT is written in Matlab and equipped with an interface to the widely used brain data analysis package SPM. The toolbox allows running fast whole-brain analyses, region-of-interest analyses and searchlight analyses, using machine learning classifiers, pattern correlation analysis, or representational similarity analysis. It offers automatic creation and visualization of diverse cross-validation schemes, feature scaling, nested parameter selection, a variety of feature selection methods, multiclass capabilities, and pattern reconstruction from classifier weights. While basic users can implement a generic analysis in one line of code, advanced users can extend the toolbox to their needs or exploit the structure to combine it with external high-performance classification toolboxes. The toolbox comes with an example data set which can be used to try out the various analysis methods. Taken together, TDT offers a promising option for researchers who want to employ multivariate analyses of brain activity patterns.

Past makes future: role of pFC in prediction

The pFC enables the essential human capacities for predicting future events and preadapting to them. These capacities rest on both the structure and dynamics of the human pFC. Structurally, pFC, together with posterior association cortex, is at the highest hierarchical level of cortical organization, harboring neural networks that represent complex goal-directed actions. Dynamically, pFC is at the highest level of the perception-action cycle, the circular processing loop through the cortex that interfaces the organism with the environment in the pursuit of goals. In its predictive and preadaptive roles, pFC supports cognitive functions that are critical for the temporal organization of future behavior, including planning, attentional set, working memory, decision-making, and error monitoring. These functions have a common future perspective and are dynamically intertwined in goal-directed action. They all utilize the same neural infrastructure: a vast array of widely distributed, overlapping, and interactive cortical networks of personal memory and semantic knowledge, named cognits, which are formed by synaptic reinforcement in learning and memory acquisition. From this cortex-wide reservoir of memory and knowledge, pFC generates purposeful, goal-directed actions that are preadapted to predicted future events.

Frontoparietal representations of task context support the flexible control of goal-directed cognition

Cognitive control allows stimulus-response processing to be aligned with internal goals and is thus central to intelligent, purposeful behavior. Control is thought to depend in part on the active representation of task information in prefrontal cortex (PFC), which provides a source of contextual bias on perception, decision making, and action. In the present study, we investigated the organization, influences, and consequences of context representation as human subjects performed a cued sorting task that required them to flexibly judge the relationship between pairs of multivalent stimuli. Using a connectivity-based parcellation of PFC and multivariate decoding analyses, we determined that context is specifically and transiently represented in a region spanning the inferior frontal sulcus during context-dependent decision making. We also found strong evidence that decision context is represented within the intraparietal sulcus, an area previously shown to be functionally networked with the inferior frontal sulcus at rest and during task performance. Rule-guided allocation of attention to different stimulus dimensions produced discriminable patterns of activation in visual cortex, providing a signature of top-down bias over perception. Furthermore, demands on cognitive control arising from the task structure modulated context representation, which was found to be strongest after a shift in task rules. When context representation in frontoparietal areas increased in strength, as measured by the discriminability of high-dimensional activation patterns, the bias on attended stimulus features was enhanced. These results provide novel evidence that illuminates the mechanisms by which humans flexibly guide behavior in complex environments.

Frontal and parietal cortical interactions with distributed visual representations during selective attention and action selection

Using multivoxel pattern analysis (MVPA), we studied how distributed visual representations in human occipitotemporal cortex are modulated by attention and link their modulation to concurrent activity in frontal and parietal cortex. We detected similar occipitotemporal patterns during a simple visuoperceptual task and an attention-to-working-memory task in which one or two stimuli were cued before being presented among other pictures. Pattern strength varied from highest to lowest when the stimulus was the exclusive focus of attention, a conjoint focus, and when it was potentially distracting. Although qualitatively similar effects were seen inside regions relatively specialized for the stimulus category and outside, the former were quantitatively stronger. By regressing occipitotemporal pattern strength against activity elsewhere in the brain, we identified frontal and parietal areas exerting top-down control over, or reading information out from, distributed patterns in occipitotemporal cortex. Their interactions with patterns inside regions relatively specialized for that stimulus category were higher than those with patterns outside those regions and varied in strength as a function of the attentional condition. One area, the frontal operculum, was distinguished by selectively interacting with occipitotemporal patterns only when they were the focus of attention. There was no evidence that any frontal or parietal area actively inhibited occipitotemporal representations even when they should be ignored and were suppressed. Using MVPA to decode information within these frontal and parietal areas showed that they contained information about attentional context and/or readout information from occipitotemporal cortex to guide behavior but that frontal regions lacked information about category identity.