Abstract: The explosive growth of the human neuroimaging literature has led to major advances in understanding of human brain function, but has also made aggregation and synthesis of neuroimaging findings increasingly difficult. Here we describe and validate a highly automated brain mapping framework that uses text mining, meta-analysis and machine learning techniques to generate a large database of mappings between neural and cognitive states. We demonstrate the capacity of our approach to (a) automatically conduct large-scale, high-quality neuroimaging meta-analyses, (b) address long-standing inferential problems in the neuroimaging literature, and (c) support accurate ?decoding? of broad cognitive states from brain activity in both entire studies and individual human subjects. Collectively, our results validate a powerful and generative new framework for synthesizing human neuroimaging data on an unprecedented scale.

Abstract: Data from self-report and observational studies make it clear that problematic social anxiety confers interpersonal constraint that may contribute to the interpersonal dysfunction reported by individuals with social anxiety disorder. Direct observation of interpersonal behavior in established relationships is rare and difficult to obtain for practical reasons. We tested a flexible iterated prisoner's dilemma computer task to examine whether it might capture interpersonal constraint related to social anxiety. Our first study suggested that, at least for men, the task directly captures social anxiety's constraint on responses to friendly giving. Our second study more strongly supported the assertion that the task directly assesses interpersonal constraint. The data support a model in which social anxiety directly confers trait-like tendencies toward interpersonal constraint and indirectly constrains behavior in specific interactions. These studies provide further evidence that constrained responses to positive social behaviors are characteristic of people with problematic social anxiety and may be particularly important to dysfunctions in the close relationships of these individuals.

Abstract: Text-analytic methods have become increasingly popular in cognitive science for understanding differences in semantic structure between documents. However, such methods have not been widely used in other disciplines. With the aim of disseminating these approaches, we introduce a text-analytic technique (Contrast Analysis of Semantic Similarity, CASS, www.casstools.org), based on the BEAGLE semantic space model (Jones & Mewhort, 2007) and add new features to test between-corpora differences in semantic associations (e.g., the association between democrat and good, compared to democrat and bad). By analyzing television transcripts from cable news from a 12 month period, we reveal significant differences in political bias between television channels (liberal to conservative: MSNBC, CNN, FoxNews) and find expected differences between newscasters (Colmes, Hannity). Compared to existing measures of media bias, our measure has higher reliability. CASS can be used to investigate semantic structure when exploring any topic (e.g., self-esteem or stereotyping) that affords a large text-based database.

Abstract: Cognitive neuroscientists increasingly recognize that continued progress in understanding human brain function will require not only the acquisition of new data, but also the synthesis and integration of data across studies and laboratories. Here we review ongoing efforts to develop a more cumulative science of human brain function. We discuss the rationale for an increased focus on formal synthesis of the cognitive neuroscience literature, provide an overview of recently developed tools and platforms designed to facilitate the sharing and integration of neuroimaging data, and conclude with a discussion of several emerging developments that hold even greater promise in advancing the study of human brain function.

Abstract: Previous studies have found systematic associations between personality and individual differences in word use. Such studies have typically focused on broad associations between major personality domains and aggregate word categories, potentially masking more specific associations. Here I report the results of a large-scale analysis of personality and word use in a large sample of blogs (N=694). The size of the dataset enabled pervasive correlations with personality to be identified for a broad range of lexical variables, including both aggregate word categories and individual English words. The results replicated category-level findings from previous offline studies, identified numerous novel associations at both a categorical and single-word level, and underscored the value of complementary approaches to the study of personality and word use.

Abstract: Investigations of individual differences have become increasingly important in the cognitive neuroscience of executive control. For instance, individual variation in lateral prefrontal cortex function (and that of associated regions) has recently been used to identify contributions of executive control processes to a number of domains, including working memory capacity, anxiety, reward/motivation, and emotion regulation. However, the origins of such individual differences remain poorly understood. Recent progress toward identifying the genetic and environmental sources of variation in neural traits, in combination with progress identifying the causal relationships between neural and cognitive processes, will be essential for developing a mechanistic understanding of executive control.

Abstract: Personality researchers have recently advocated the use of very short personality inventories in order to minimize administration time. However, few such inventories are currently available. Here I introduce an automated method that can be used to abbreviate virtually any personality inventory with minimal effort. After validating the method against existing measures in Studies 1 and 2, a new 181-item inventory is generated in Study 3 that accurately recaptures scores on 8 different broadband inventories comprising 203 distinct scales. Collectively, the results validate a powerful new way to improve the efficiency of personality measurement in research settings.

Abstract: Vul et al argue that correlations in many cognitive neuroscience studies are grossly inflated due to a widespread tendency to employ non-independent analyses. Here I argue that Vul et al's primary conclusion is correct, but for different reasons than they suggest. I demonstrate that the primary cause of grossly inflated correlations in whole-brain fMRI analyses is not non-independence, but the pernicious combination of small sample sizes and stringent alpha-correction levels. Far from defusing Vul et al's conclusions, the simulations presented suggest that the level of inflation may be even worse than Vul et al's empirical analysis would suggest.

Abstract:

Background: Reaction time (RT) is one of the most widely used measures of performance in experimental psychology, yet relatively few fMRI studies have included trial-by-trial differences in RT as a predictor variable in their analyses. Using a multi-study approach, we investigated whether there are brain regions that show a general relationship between trial-by-trial RT variability and activation across a range of cognitive tasks.

Methodology/Principal Findings: The relation between trial-by-trial differences in RT and brain activation was modeled in five different fMRI datasets spanning a range of experimental tasks and stimulus modalities. Three main findings were identified. First, in a widely distributed set of gray and white matter regions, activation was delayed on trials with long RTs relative to short RTs, suggesting delayed initiation of underlying physiological processes. Second, in lateral and medial frontal regions, activation showed a “time-on-task” effect, increasing linearly as a function of RT. Finally, RT variability reliably modulated the BOLD signal not only in gray matter but also in diffuse regions of white matter.

Conclusions/Significance: The results highlight the importance of modeling trial-by-trial RT in fMRI analyses and raise the possibility that RT variability may provide a powerful probe for investigating the previously elusive white matter BOLD signal.

Abstract: Visual word recognition studies commonly measure the orthographic similarity of words using Coltheart's orthographic neighborhood size metric (ON). Although ON reliably predicts behavioral variability in many lexical tasks, its utility is inherently limited by its relatively restrictive definition. Here we introduce a new measure of orthographic similarity generated using a standard computer science metric of string similarity (Levenshtein distance). Unlike ON, the new measure, named Orthographic Levenshtein Distance 20 (OLD20), incorporates comparisons between all pairs of words in the lexicon, including words of different lengths. We demonstrate that OLD20 provides significant advantages over ON in predicting both lexical decision and pronunciation performance in three large datasets. Moreover, OLD20 interacts more strongly with word frequency and shows stronger effects of neighborhood frequency than ON. Discussion focuses on the implications of these results for models of visual word recognition.

Abstract: Reading is one of the most important skills human beings can acquire, but has proven difficult to study naturalistically using functional magnetic resonance imaging (fMRI). We introduce a novel Event-Related Reading (ERR) fMRI approach that enables reliable estimation of the neural correlates of single-word processing during reading of rapidly presented narrative text (200-300 ms / word). Application to an fMRI experiment in which subjects read coherent narratives and made no overt responses revealed widespread effects of orthographic, phonological, contextual, and semantic variables on brain activation. Word-level variables predicted activity in classical language areas as well as the inferotemporal visual word form area, specifically supporting a role for the latter in mapping visual forms onto articulatory or acoustic representations. Additional analyses demonstrated that ERR results replicate across experiments and predict reading comprehension. The ERR approach represents a powerful and extremely flexible new approach for studying reading and language behavior with fMRI.

Abstract: When reading a narrative, comprehension and retention of information benefit considerably from the use of situation models—coherent representations of the characters, locations, and activities described in the text. Here we used functional magnetic resonance imaging (fMRI) to explore the neural mechanisms supporting situation model processing. Participants read blocks of sentences that were either unrelated to one another or formed coherent narratives. A timecourse-based approach was used to identify regions that differentiated narrative-level comprehension from sentence-level comprehension. Most brain regions that showed modulation of activation during narrative-level comprehension were also modulated to a lesser extent during sentence-level comprehension, suggesting a shared reliance on general coherence-building mechanisms. However, tentative evidence was found for narrative-specific activation in dorsomedial prefrontal cortex. Additional analyses identified spatiotemporally distinct neural contributions to situation model processing, with posterior parietal regions supporting situation model construction and frontotemporal regions supporting situation model maintenance. Finally, a set of subsequent memory analyses demonstrated that the boost in comprehension and memory performance observed for coherent materials was attributable to the use of integrative situation models rather than lower-level differences in sentence-level or word-level encoding. These results clarify the functional contributions of distinct brain systems to situation model processing and their mapping onto existing psychological models of narrative comprehension.

Abstract: The human amygdala has classically been viewed as a brain structure primarily related to emotions and dissociated from higher cognition. We report here findings suggesting that the human amygdala also has a role in supporting working memory (WM), a canonical higher cognitive function. In a first functional magnetic resonance imaging (fMRI) study (n = 53), individual differences in amygdala activity predicted behavioral performance in a 3-backWMtask. Specifically, higher event-related amygdala amplitude predicted faster response time (RT; r = -0.64) with no loss of accuracy. This relationship was not contingent on mood state, task content, or personality variables. In a second fMRI study (n = 21), we replicated the key finding (r = -0.47) and further showed that the correlation between the amygdala and faster RT was specific to a high working memory load condition (3-back) compared with a low working memory load condition (1-back). These results support models of amygdala function that can account for its involvement not only in emotion but also higher cognition.

Abstract: Although functional neuroimaging studies of human decision-making processes are increasingly common, most of the research in this area has relied on passive tasks that generate little individual variability. Relatively little attention has been paid to the ability of brain activity to predict overt behavior. Using functional magnetic resonance imaging (fMRI), we investigated the neural mechanisms underlying behavior during a dynamic decision task that required subjects to select smaller, short-term monetary payoffs in order to receive larger, long-term gains. The number of trials over which the long-term gains accrued was manipulated experimentally (2 versus 12). Event-related neural activity in right lateral prefrontal cortex, a region associated with high-level cognitive processing, selectively predicted choice behavior in both conditions, whereas insular cortex responded to fluctuations in amount of reward but did not predict choice behavior. These results demonstrate the utility of a functional neuroimaging approach in behavioral psychology, showing that (a) highly circumscribed brain regions are capable of predicting complex choice behavior, and (b) fMRI has the ability to dissociate the contributions of different neural mechanisms to particular behavioral tasks.

Abstract: To test for a relation between individual differences in personality and neural processing efficiency, we used fMRI to assess brain activity within regions associated with cognitive control during a demanding working memory task. Fifty-three participants completed both the self-report BIS-BAS personality scales and a standard measure of fluid intelligence (Raven's Advanced Progressive Matrices). They were then scanned as they performed a 3-back working memory task. A mixed blocked / event-related fMRI design enabled us to identify both sustained and transient neural activity. Higher BAS was negatively related to event-related activity in dorsal anterior cingulate, lateral prefrontal cortex, and parietal areas in regions of interest identified in previous work. These relationships were not explained by differences in either behavioral performance or fluid intelligence, consistent with greater neural efficiency. The results reveal high specificity of the relationships among personality, cognition, and brain activity. The data confirm that affective dimensions of personality are independent of intelligence, yet also suggest that they might be inter-related in subtle ways, because they modulate activity in overlapping brain regions that appear critical for task performance.

Abstract: Decision-making has both cognitive and affective components, but previous neuroimaging studies in this domain predominantly have focused on affect and reward. The current study examined a decision-making paradigm that placed strong demands on cognitive control processes by making reward payoffs contingent upon decision-making history. Payoffs were maximized by choosing the option that, paradoxically, was associated with a lower payoff on the immediate trial. Temporal integration requirements were manipulated by varying, across conditions, the window of previous trials over which the reward function was calculated. The cognitive demands of the task were hypothesized to engage neural systems responsible for integrating actions and outcomes over time, and involving active maintenance and top-down biasing of response selection. Brain activation was monitored with functional magnetic resonance imaging (fMRI) using a mixed-blocked and event-related design to extract both transient and sustained neural responses. A network of brain regions commonly associated with cognitive control functions, including bilateral prefrontal cortex (PFC), bilateral parietal cortex, and medial frontal cortex, showed selectively sustained activation during the task. Increasing temporal integration demands led to a shift from transient to sustained activity in additional regions, including right hemisphere dorsolateral and frontopolar PFC. These results demonstrate the contribution of cognitive control mechanisms to temporally extended decision-making paradigms and highlight the benefits of decomposing activation responses into sustained and transient components.