James K. Kroger

Rostrolateral Prefrontal Cortex Involvement in Relational Integration during Reasoning

Patient and neuroimaging studies indicate that complex reasoning tasks are associated with the prefrontal cortex (PFC). In this study, we tested the hypothesis that the process of relational integration, or considering multiple relations simultaneously, is a component process of complex reasoning that selectively recruits PFC. We used fMRI to examine brain activation during 0-relational, 1-relational, and 2-relational problems adapted from the Ravens Progressive Matrices and hypothesized that PFC would be preferentially recruited by the 2-relational problem type. Event-related responses were modeled by convolving a canonical hemodynamic response function with the response time (RT) associated with each trial. The results across different analyses revealed the same pattern: PFC activation was specific to the comparison between 2- and 1-relational problems and was not observed in the comparison between 1- and 0-relational problems. Furthermore, the process of relational integration was specifically associated with bilateral rostrolateral PFC (RLPFC; lateral area 10) and right dorsolateral PFC (areas 9 and 46). Left RLPFC showed the greatest specificity by remaining preferentially recruited during 2-relational problems even after comparisons were restricted to trials matched for RT and accuracy. The link between RLPFC and the process of relational integration may be due to the associated process of manipulating self-generated information, a process that may characterize RLPFC function.

Cross-modal and cross-temporal association in neurons of frontal cortex.

The prefrontal cortex is essential for the temporal integration of sensory information in behavioural and linguistic sequences. Such information is commonly encoded in more than one sense modality, notably sight and sound. Connections from sensory cortices to the prefrontal cortex support its integrative function. Here we present the first evidence that prefrontal cortex cells associate visual and auditory stimuli across time. We gave monkeys the task of remembering a tone of a certain pitch for 10 s and then choosing the colour associated with it. In this task, prefrontal cortex cells responded selectively to tones, and most of them also responded to colours according to the task rule. Thus, their reaction to a tone was correlated with their subsequent reaction to the associated colour. This correlation faltered in trials ending in behavioural error. We conclude that prefrontal cortex neurons are part of integrative networks that represent behaviourally meaningful cross-modal associations. The orderly and timely activation of neurons in such networks is crucial for the temporal transfer of information in the structuring of behaviour, reasoning and language.

Auditory memory cells in dorsolateral prefrontal cortex

The activity of single neurons was recorded extracellularly from dorsolateral prefrontal cortex (DPC) of monkeys during the performance of a cross-modal audio-visual short-term memory task. Cells in DPC show sustained elevated firing levels (higher than spontaneous discharge) during the retention of the auditory stimulus. In some cells this elevated firing was significantly different depending on the particular auditory memorandum of each trial. These results support the notion that DPC participates in auditory short-term memory and the integration of auditory and visual information for prospective action.

Distinct neural substrates for deductive and mathematical processing

In an effort to clarify how deductive reasoning is accomplished, an fMRI study was performed to observe the neural substrates of logical reasoning and mathematical calculation. Participants viewed a problem statement and three premises, and then either a conclusion or a mathematical formula. They had to indicate whether the conclusion followed from the premises, or to solve the mathematical formula. Language areas of the brain (Brocas and Wernickes area) responded as the premises and the conclusion were read, but solution of the problems was then carried out by non-language areas. Regions in right prefrontal cortex and inferior parietal lobe were more active for reasoning than for calculation, whereas regions in left prefrontal cortex and superior parietal lobe were more active for calculation than for reasoning. In reasoning, only those problems calling for a search for counterexamples to conclusions recruited right frontal pole. These results have important implications for understanding how higher cognition, including deduction, is implemented in the brain. Different sorts of thinking recruit separate neural substrates, and logical reasoning goes beyond linguistic regions of the brain.

A Functional Dissociation of Conflict Processing Within Anterior Cingulate Cortex

Goal‐directed behavior requires cognitive control to regulate the occurrence of conflict. The dorsal anterior cingulate cortex (dACC) has been suggested in detecting response conflict during various conflict tasks. Recent findings, however, have indicated not only that two distinct subregions of dACC are involved in conflict processing but also that the conflict occurs at both perceptual and response levels. In this study, we sought to examine whether perceptual and response conflicts are functionally dissociated in dACC. Thirteen healthy subjects performed a version of the Stroop task during functional magnetic resonance imaging (fMRI) scanning. We identified a functional dissociation of the caudal dACC (cdACC) and the rostral dACC (rdACC) in their responses to different sources of conflict. The cdACC was selectively engaged in perceptual conflict whereas the rdACC was more active in response conflict. Further, the dorsolateral prefrontal cortex (DLPFC) was coactivated not with cdACC but with rdACC. We suggest that cdACC plays an important role in regulative processing of perceptual conflict whereas rdACC is involved in detecting response conflict. Hum Brain Mapp, 2011.

Recreating Raven's: Software for systematically generating large numbers of Raven-like matrix problems with normed properties

Raven’s Progressive Matrices is a widely used test for assessing intelligence and reasoning ability (Raven, Court, & Raven, 1998). Since the test is nonverbal, it can be applied to many different populations and has been used all over the world (Court & Raven, 1995). However, relatively few matrices are in the sets developed by Raven, which limits their use in experiments requiring large numbers of stimuli. For the present study, we analyzed the types of relations that appear in Raven’s original Standard Progressive Matrices (SPMs) and created a software tool that can combine the same types of relations according to parameters chosen by the experimenter, to produce very large numbers of matrix problems with specific properties. We then conducted a norming study in which the matrices we generated were compared with the actual SPMs. This study showed that the generated matrices both covered and expanded on the range of problem difficulties provided by the SPMs.

Evoking the Permission Schema: The Impact of Explicit Negation and a Violation-checking Context

Cheng and Holyoak (1985) proposed that realistic reasoning in deontic contexts is based on pragmatic schemas such as those for assessing compliance with or violation of permission and obligation rules, and that the evocation of these schemas can facilitate performance in Wasons (1966) selection task. The inferential rules in such schemas are intermediate in generality between the content-independent rules proposed by logicians and specific cases stored in memory. In one test of their theory, Cheng and Holyoak demonstrated that facilitation could be obtained even for an abstract permission rule that is devoid of concrete thematic content. Jackson and Griggs (1990) argued on the basis of several experiments that such facilitation is not due to evocation of a permission schema, but, rather, results from a combination of presentation factors: the presence of explicit negatives in the statement of cases and the presence of a violation-checking context. Their conclusion calls into question both the generality of content effects in reasoning and the explanation of these effects. We note that Jackson and Griggs did not test whether the same combination of presentation factors would produce facilitation for an arbitrary rule that does not involve deontic concepts, as their proposal would predict. The present study tested this prediction. Moreover, we extended Jackson and Griggs’ comparisons between performance with an abstract permission rule versus an arbitrary rule, introducing clarifications in the statement of each. No facilitation was observed for an arbitrary rule even when explicit negatives and a violation-checking context were used, whereas strong facilitation was found for the abstract permission rule under the same conditions. Performance on the arbitrary rule was not improved even when the instructions indicated that the rule was conditional rather than biconditional. In contrast, a small but reliable degree of facilitation was obtained for the abstract permission rule, with violation-checking content even in the absence of explicit negatives. The theory of pragmatic reasoning schemas can account for both the present findings and those reported by Jackson and Griggs.

Novel beamformers for Multiple Correlated brain source localization and reconstruction

This paper introduces two novel beamforming algorithms, namely the Region Constrained and Multiple Correlated Source Model beamformers, designed to localize and to reconstruct highly correlated brain sources from noisy EEG data. Multiple correlated source simulations have been performed to evaluate the performance of the proposed algorithms, using a realistic 176 × 240 × 256 finite difference head model. Our simulation results show that the Region Constrained-Multiple Correlated Source Model beamformer, obtained by combining the above two beamformers, allows us to localize three perfectly correlated brain sources with very high localization accuracy. Finally, the eigenspace version of this beamformer can be used to reconstruct three correlated brain source signals correctly from simulated noisy EEG data.

Novel vector beamformers for EEG source imaging

This paper introduces two novel vector beamforming algorithms, namely the Vector Weight Normalized and Vector Standardized Minimum Variance beamformers, for brain source localization and reconstruction. Our mathematical analysis shows that the Vector Weight Normalized Minimum Variance beamformer (V-WNMVB) is the true vector version of the Synthetic Aperture Magnetoencephalography (SAM). Our Monte-Carlo simulation results with fixed and rotated dipole sources show that the two new vector beamformers give better source localization errors than the existing ones, including SAM, linearly constrained minimum variance beamformer and vector Borgiotti-Kaplan beamformer. Finally, the multiple dipole source simulation studies show that the performance of V-WNMVB is as good as that of SAM, however, it does not require any assumption on the source orientation.