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Objective Transcranial magnetic stimulation (TMS) is a non-invasive tool used for studying cortical excitability and plasticity in the human brain. This review aims to quantitatively synthesize the literature on age-related differences in cortical excitability and plasticity, examined by TMS. Methods A literature search was conducted using MEDLINE, Embase, and PsycINFO from 1980 to December 2015. We extracted studies with healthy old (50–89 years) versus young (16–49 years) individuals that utilized the following TMS measures: resting motor threshold (RMT), short-interval cortical inhibition (SICI), short-latency afferent inhibition (SAI), cortical silent period (CSP), intracortical facilitation (ICF), and paired associative stimulation (PAS). Results We found a significant increase in RMT (g = 0.414, 95% confidence interval (CI) [0.284, 0.544], p<0.001), a significant decrease in SAI (g=0.778, 95% CI [0.478, 1.078], p<0.001), and a trending decrease in LTP-like plasticity (g=−0.528, 95% CI [−1.157, 0.100 p<0.1) with age. Conclusions Our findings suggest an age-dependent reduction in cortical excitability and sensorimotor integration within the human motor cortex. Significance Alterations in the ability to regulate cortical excitability, sensorimotor integration and plasticity may underlie several age-related motor deficits.

Compositionality, or the ability to build complex cognitive structures from simple parts, is fundamental to the power of the human mind. Here we relate this principle to the psychometric concept of fluid intelligence, traditionally measured with tests of complex reasoning. Following the principle of compositionality, we propose that the critical function in fluid intelligence is splitting a complex whole into simple, separately attended parts. To test this proposal, we modify traditional matrix reasoning problems to minimize requirements on information integration, working memory, and processing speed, creating problems that are trivial once effectively divided into parts. Performance remains poor in participants with low fluid intelligence, but is radically improved by problem layout that aids cognitive segmentation. In line with the principle of compositionality, we suggest that effective cognitive segmentation is important in all organized behavior, explaining the broad role of fluid intelligence in successful cognition.fluid intelligence | problem solving | cognitive compositionally | focused attention

HighlightsFluid intelligence links closely to goal neglect in novel behaviour.Neglect increases strongly with task complexity.The task complexity effect is bounded by task-subtask structure.We argue that complex behaviour is controlled in a series of attentional episodes.A new account of fluid intelligence is based on episode construction.

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.

knowledge about the tasks we encounter enables us to rapidly and flexibly adapt to novel task contexts. Previous research has focused primarily on abstract rules that leverage shared structure in stimulus-response (S-R) mappings as the basis of such task knowledge. Here we provide evidence that working memory (WM) gating policies – a type of control policy required for internal control of WM during a task – constitute a form of abstract task knowledge that can be transferred across contexts. In two experiments, we report specific evidence for the transfer of selective WM gating policies across changes of task context. We show that this transfer is not tied to shared structure in S-R mappings, but instead in the dynamic structure of the task. Collectively, our results highlight the importance of WM gating policies in particular, and control policies in general, as a key component of the task knowledge that supports flexible behavior and task generalization.

The neurobiology underlying depression in older adults is less extensively evaluated than in younger adults, despite the putative influence of aging on depression neuropathology. Studies using transcranial magnetic stimulation (TMS), a neurophysiological tool capable of probing inhibitory and excitatory cortical neurotransmission, have identified dysfunctional GABAergic inhibitory activity in younger adults with depression. However, GABAergic and glutamatergic cortical neurotransmission have not yet been studied in late-life depression (LLD). Here, we used single- and paired-pulse TMS to measure cortical inhibition and excitation in 92 LLD patients and 41 age-matched healthy controls. To differentiate the influence of age and depression, we also compared these TMS indices to those of 30 younger depressed adults and 30 age- and sex-matched younger healthy adults. LLD patients, older healthy adults, and younger depressed adults demonstrated significantly lower GABA receptor-mediated cortical inhibition than younger healthy controls. By contrast, no significant differences in cortical inhibition were observed between older adults with and without depression. No significant differences in GABA receptor-mediated inhibition or cortical excitation were found between the groups. Altogether, these findings suggest that reduced cortical inhibition may be associated with both advancing age and depression, which (i) supports the model of depression as a disease of accelerated aging, and (ii) prompts future investigation into diminished GABAergic neurotransmission in late-life as a biological predisposing factor to the development of depression. Given that cortical neurophysiology was similar in depressed and healthy older adults, future prospective studies need to establish the relative influence of age and depression on cortical inhibition deficits.

In order to navigate a complex web of relationships, an individual must learn and represent the connections between people in a social network. However, the sheer size and complexity of the social world makes it impossible to acquire firsthand knowledge of all relations within a network, suggesting that people must make inferences about unobserved relationships to fill in the gaps. Across three studies (n = 328), we show that people can encode information about social features (e.g., hobbies, clubs) and subsequently deploy this knowledge to infer the existence of unobserved friendships in the network. Using computational models, we test various feature-based mechanisms that could support such inferences. We find that people’s ability to successfully generalize depends on two representational strategies: a simple but inflexible similarity heuristic that leverages homophily, and a complex but flexible cognitive map that encodes the statistical relationships between social features and friendships. Together, our studies reveal that people can build cognitive maps encoding arbitrary patterns of latent relations in many abstract feature spaces, allowing social networks to be represented in a flexible format. Moreover, these findings shed light on open questions across disciplines about how people learn and represent social networks and may have implications for generating more human-like link prediction in machine learning algorithms.