Intelligence and uncertainty: Implications of hierarchical predictive processing for the neuroscience of cognitive ability

Spontaneously emerging internal models of visual sequences combine abstract and event-specific information in the prefrontal cortex

When exposed to sensory sequences, do macaque monkeys spontaneously form abstract internal models that generalize to novel experiences? Here, we show that neuronal populations in macaque ventrolateral prefrontal cortex jointly encode visual sequences by separate codes for the specific pictures presented and for their abstract sequential structure. We recorded prefrontal neurons while macaque monkeys passively viewed visual sequences and sequence mismatches in the local-global paradigm. Even without any overt task or response requirements, prefrontal populations spontaneously form representations of sequence structure, serial order, and image identity within distinct but superimposed neuronal subspaces. Representations of sequence structure rapidly update following single exposure to a mismatch sequence, while distinct populations represent mismatches for sequences of different complexity. Finally, those representations generalize across sequences following the same repetition structure but comprising different images. These results suggest that prefrontal populations spontaneously encode rich internal models of visual sequences reflecting both content-specific and abstract information.

How robust is the relationship between neural processing speed and cognitive abilities?

Individual differences in processing speed are consistently related to individual differences in cognitive abilities, but the mechanisms through which a higher processing speed facilitates reasoning remain largely unknown. To identify these mechanisms, researchers have been using latencies of the event-related potential (ERP) to study how the speed of cognitive processes associated with specific ERP components is related to cognitive abilities. Although there is some evidence that latencies of ERP components associated with higher-order cognitive processes are related to intelligence, results are overall quite inconsistent. These inconsistencies likely result from variations in analytic procedures and little consideration of the psychometric properties of ERP latencies in relatively small sample studies. Here we used a multiverse approach to evaluate how different analytical choices regarding references, low-pass filter cutoffs, and latency measures affect the psychometric properties of P2, N2, and P3 latencies and their relations with cognitive abilities in a sample of 148 participants. Latent correlations between neural processing speed and cognitive abilities ranged from -.49 to -.78. ERP latency measures contained about equal parts of measurement error variance and systematic variance, and only about half of the systematic variance was related to cognitive abilities, whereas the other half reflected nuisance factors. We recommend addressing these problematic psychometric properties by recording EEG data from multiple tasks and modeling relations between ERP latencies and covariates in latent variable models. All in all, our results indicate that there is a substantial and robust relationship between neural processing speed and cognitive abilities when those issues are addressed.

Anticipation across modalities in children and adults: Relating anticipatory alpha rhythm lateralization, reaction time, and executive function

The development of the ability to anticipate-as manifested by preparatory actions and neural activation related to the expectation of an upcoming stimulus-may play a key role in the ontogeny of cognitive skills more broadly. This preregistered study examined anticipatory brain potentials and behavioral responses (reaction time; RT) to anticipated target stimuli in relation to individual differences in the ability to use goals to direct action (as indexed by measures of executive function; EF). A cross-sectional investigation was conducted in 40 adults (aged 18-25 years) and 40 children (aged 6-8 years) to examine the association of changes in the amplitude of modality-specific alpha-range rhythms in the electroencephalogram (EEG) during anticipation of lateralized visual, tactile, or auditory stimuli with inter- and intraindividual variation in RT and EF. Children and adults exhibited contralateral anticipatory reductions in the mu rhythm and the visual alpha rhythm for tactile and visual anticipation, respectively, indicating modality and spatially specific attention allocation. Variability in within-subject anticipatory alpha lateralization (the difference between contralateral and ipsilateral alpha power) was related to single-trial RT. This relation was more prominent in adults than in children, and was not apparent for auditory stimuli. Multilevel models indicated that interindividual differences in anticipatory mu rhythm lateralization contributed to the significant association with variability in EF, but this was not the case for visual or auditory alpha rhythms. Exploratory microstate analyses were undertaken to cluster global field power (GFP) into a distribution-free temporal analysis examining developmental differences across samples and in relation to RT and EF. Anticipation is suggested as a developmental bridge construct connecting neuroscience, behavior, and cognition, with anticipatory EEG oscillations being discussed as quantifiable and potentially malleable indicators of stimulus prediction.

Intelligence and Visual Mismatch Negativity: Is Pre-Attentive Visual Discrimination Related to General Cognitive Ability?

EEG has been used for decades to identify neurocognitive processes related to intelligence. Evidence is accumulating for associations with neural markers of higher-order cognitive processes (e.g., working memory); however, whether associations are specific to complex processes or also relate to earlier processing stages remains unclear. Addressing these issues has implications for improving our understanding of intelligence and its neural correlates. The MMN is an ERP that is elicited when, within a series of frequent standard stimuli, rare deviant stimuli are presented. As stimuli are typically presented outside the focus of attention, the MMN is suggested to capture automatic pre-attentive discrimination processes. However, the MMN and its relation to intelligence has largely only been studied in the auditory domain, thus preventing conclusions about the involvement of automatic discrimination processes in humans' dominant sensory modality-vision. EEG was recorded from 50 healthy participants during a passive visual oddball task that presented simple sequence violations and deviations within a more complex hidden pattern. Signed area amplitudes and fractional area latencies of the visual MMN were calculated with and without Laplacian transformation. Correlations between visual MMN and intelligence (Raven's Advanced Progressive Matrices) were of negligible to small effect sizes, differed critically between measurement approaches, and Bayes Factors provided anecdotal to substantial evidence for the absence of an association. We discuss differences between the auditory and visual MMN, the implications of different measurement approaches, and offer recommendations for further research in this evolving field.

Disconnection from prediction: A systematic review on the role of right temporoparietal junction in aberrant predictive processing

The right temporoparietal junction (rTPJ) is a brain area that plays a critical role in a variety of cognitive functions. Although different theoretical proposals tried to explain the ubiquitous role of rTPJ, recent evidence suggests that rTPJ may be a fundamental cortical region involved in different kinds of predictions. This systematic review aims to better investigate the potential role of rTPJ under a predictive processing perspective, providing an overview of cognitive impairments in neurological patients as the consequence of structural or functional disconnections or damage of rTPJ. Results confirm the involvement of rTPJ across several tasks and neurological pathologies. RTPJ, via its connections with other brain networks, would integrate diverse information and update internal models of the world. Against traditional views, which tend to focus on distinct domains, we argue that the role of rTPJ can be parsimoniously interpreted as a key hub involved in domain-general predictions. This alternative account of rTPJ role in aberrant predictive processing opens different perspectives, stimulating new hypotheses in basic research and clinical contexts.

g versus c: comparing individual and collective intelligence across two meta-analyses

Collective intelligence (CI) is said to manifest in a group's domain general mental ability. It can be measured across a battery of group IQ tests and statistically reduced to a latent factor called the "c-factor." Advocates have found the c-factor predicts group performance better than individual IQ. We test this claim by meta-analyzing correlations between the c-factor and nine group performance criterion tasks generated by eight independent samples (N = 857 groups). Results indicated a moderate correlation, r, of .26 (95% CI .10, .40). All but four studies comprising five independent samples (N = 366 groups) failed to control for the intelligence of individual members using individual IQ scores or their statistically reduced equivalent (i.e., the g-factor). A meta-analysis of this subset of studies found the average IQ of the groups' members had little to no correlation with group performance (r = .06, 95% CI -.08, .20). Around 80% of studies did not have enough statistical power to reliably detect correlations between the primary predictor variables and the criterion tasks. Though some of our findings are consistent with claims that a general factor of group performance may exist and relate positively to group performance, limitations suggest alternative explanations cannot be dismissed. We caution against prematurely embracing notions of the c-factor unless it can be independently and robustly replicated and demonstrated to be incrementally valid beyond the g-factor in group performance contexts.

Spontaneous Network Coupling Enables Efficient Task Performance without Local Task-Induced Activations

Neurobehavioral studies in humans have long concentrated on changes in local activity levels during repetitive executions of a task. Spontaneous neural coupling within extended networks has latterly been found to also influence performance. Here, we intend to uncover the underlying mechanisms, the relative importance, and the interaction between spontaneous coupling and task-induced activations. To do so, we recorded two groups of healthy participants (male and female) during rest and while they performed either a visual perception or a motor sequence task. We demonstrate that, for both tasks, stronger activations during the task as well as greater network coupling through spontaneous α rhythms at rest predict performance. However, high performers present an absence of classical task-induced activations and, instead, stronger spontaneous network coupling. Activations were thus a compensation mechanism needed only in subjects with lower spontaneous network interactions. This challenges classical models of neural processing and calls for new strategies in attempts to train and enhance performance.SIGNIFICANCE STATEMENT Our findings challenge the widely accepted notion that task-induced activations are of paramount importance for behavior. This will have an important impact on interpretations of human neurobehavioral research. They further link the widely used techniques of quantifying network communication in the brain with classical neuroscience methods and demonstrate possible ways of how network communication influences human behavior. Traditional training methods attempt to enhance neural activations through task repetitions. Our findings suggest a more efficient neural target for learning: enhancing spontaneous neural interactions. This will be of major interest for a large variety of scientific fields with very broad applications in schools, work, and others.

Integrated Intelligence from Distributed Brain Activity

How does organized cognition arise from distributed brain activity? Recent analyses of fluid intelligence suggest a core process of cognitive focus and integration, organizing the components of a cognitive operation into the required computational structure. A cortical 'multiple-demand' (MD) system is closely linked to fluid intelligence, and recent imaging data define nine specific MD patches distributed across frontal, parietal, and occipitotemporal cortex. Wide cortical distribution, relative functional specialization, and strong connectivity suggest a basis for cognitive integration, matching electrophysiological evidence for binding of cognitive operations to their contents. Though still only in broad outline, these data suggest how distributed brain activity can build complex, organized cognition.

A Meta-Analysis of Relationships between Measures of Wisconsin Card Sorting and Intelligence

The Wisconsin Card Sorting Test (WCST) represents a widely utilized neuropsychological assessment technique for executive function. This meta-analysis examined the discriminant validity of the WCST for the assessment of mental shifting, considered as an essential subcomponent of executive functioning, against traditional psychometric intelligence tests. A systematic search was conducted, resulting in 72 neuropsychological samples for the meta-analysis of relationships between WCST scores and a variety of intelligence quotient (IQ) domains. The study revealed low to medium-sized correlations with IQ domains across all WCST scores that could be investigated. Verbal/crystallized IQ and performance/fluid IQ were indistinguishably associated with WCST scores. To conclude, the WCST assesses cognitive functions that might be partially separable from common conceptualizations of intelligence. More vigorous initiatives to validate putative indicators of executive function against intelligence are required.

Neural anticipatory mechanisms predict faster reaction times and higher fluid intelligence

Higher cognitive ability is reliably linked to better performance on chronometric tasks (i.e., faster reaction times, RT), yet the neural basis of these effects remains unclear. Anticipatory processes represent compelling yet understudied potential mechanisms of these effects, which may facilitate performance through reducing the uncertainty surrounding the temporal onset of stimuli (temporal uncertainty) and/or facilitating motor readiness despite uncertainty about impending target locations (target uncertainty). Specifically, the contingent negative variation (CNV) represents a compelling candidate mechanism of anticipatory motor planning, while the alpha oscillation is thought to be sensitive to temporal contingencies in perceptual systems. The current study undertook a secondary analysis of a large data set (n = 91) containing choice RT, cognitive ability, and EEG measurements to help clarify these issues. Single-trial EEG analysis in conjunction with mixed-effects modeling revealed that higher fluid intelligence corresponded to faster RT on average. When considered together, temporal and target uncertainty moderated the RT-ability relationship, with higher ability being associated with greater resilience to both types of uncertainty. Target uncertainty attenuated the amplitude of the CNV for all participants, but higher ability individuals were more resilient to this effect. Similarly, only higher ability individuals showed increased prestimulus alpha power (at left-lateralized sites) during longer, more easily anticipated interstimulus intervals. Collectively, these findings emphasize top-down anticipatory processes as likely contributors to chronometry-ability correlations.