Neural Representations in Context

SummaryThe neural responses that distinguish novel from familiar items in recognition memory tasks are remarkably fast in both humans and nonhuman primates. In humans, the earliest onsets of neural novelty effects emerge at about ∼150–200 ms after stimulus onset [1–5]. However, in recognition memory studies with nonhuman primates, novelty effects can arise at as early as 70–80 ms [6, 7]. Here, we address the possibility that this large species difference in onset latencies is caused experimentally by the necessity of using reward reinforcement to motivate the detection of novel or familiar items in nonhuman primates but not in humans. Via magnetoencephalography in humans, we show in two experiments that the onset of neural novelty signals is accelerated from ∼200 ms to ∼85 ms if correct recognition memory for either novel or familiar items is rewarded. Importantly, this acceleration is independent of whether the detection of the novel or the familiar scenes is rewarded. Furthermore, this early novelty effect contributed to memory retrieval because neural reward responses, which were contingent upon novelty detection, followed ∼100 ms later. Thus, under the contextual influence of reward motivation, behaviorally relevant novelty signals emerge much faster than previously held possible in humans.

Sensory experience is influenced by emotional context. Although perception of emotion and unpleasant visceral sensation are associated with activation within the insula and dorsal and ventral anterior cingulate gyri (ACG), regions important for attention to and perception of sensory and emotional information, the neural mechanisms underlying the effect of emotional context upon visceral sensation remain unexplored. Using functional MRI, we examined neural responses to phasic, non-painful oesophageal sensation (OS) in eight healthy subjects (seven male; age range 27-36 years) either during neutral or negative emotional contexts produced, respectively, by presentation of neutral or fearful facial expressions. Activation within right insular and bilateral dorsal ACG was significantly greater (P < 0.01) during OS with fearful than with neutral faces. In a second experiment, we measured anxiety, discomfort and neural responses in eight healthy male subjects (age range 22-41 years) to phasic, non-painful OS during presentation of faces depicting either low, moderate or high intensities of fear. Significantly greater (P < 0.01) discomfort, anxiety and activation predominantly within the left dorsal ACG and bilateral anterior insulae occurred with high-intensity compared with low-intensity expressions. Clusters of voxels were also detected in this region, which exhibited a positive correlation between subjective behaviour and blood oxygenation level-dependent effect (P < 0.05). We report the first evidence for a modulation of neural responses, and perceived discomfort during, non-painful visceral stimulation by the intensity of the negative emotional context in which the stimulation occurs, and suggest a mechanism for the effect of negative context on symptoms in functional pain disorders.

Multivariate pattern analysis can address many of the challenges for cognitive neuroscience highlighted in After Phrenology (Anderson 2014) by illuminating the information content of brain regions and by providing insight into whether functional overlap reflects the recruitment of common or distinct computational mechanisms. Further, failing to consider submaximal but reliable population responses can lead to an overly modular account of brain function.

Here, we ask which neural regions have neural response patterns that correlate with behavioral performance in a visual processing task. We found that the representational structure across all of high-level visual cortex has the requisite structure to predict behavior. Furthermore, when directly comparing different neural regions, we found that they all had highly similar category-level representational structures. These results point to a ubiquitous and uniform representational structure in high-level visual cortex underlying visual object processing.

Dual coding of knowledge in the human brain.

THINGS-data reflects three large-scale neuroimaging and behavioral datasets of object processing in humans, comprising densely sampled functional MRI and magnetoencephalographic recordings, as well as 4.70 million similarity judgments in response to thousands of photographic images for up to 1854 objects.

We present experimental evidence in support of distributed neural codes for timbre that are implicated in discrimination of musical styles. We used functional magnetic resonance imaging (fMRI) in humans and multivariate pattern analysis (MVPA) to identify activation patterns that encode the perception of rich music audio stimuli from five different musical styles. We show that musical styles can be automatically classified from population codes in bilateral superior temporal sulcus (STS). To investigate the possible link between the acoustic features of the auditory stimuli and neural population codes in STS, we conducted a representational similarity analysis and a multivariate regression-retrieval task. We found that the similarity structure of timbral features of our stimuli resembled the similarity structure of the STS more than any other type of acoustic feature. We also found that a regression model trained on timbral features outperformed models trained on other types of audio features. Our results show that human brain responses to complex, natural music can be differentiated by timbral audio features, emphasizing the importance of timbre in auditory perception.

A controversial issue in neurolinguistics is whether basic neural auditory representations found in many animals can account for human perception of speech. This question was addressed by examining how a population of neurons in the primary auditory cortex (A1) of the naive awake ferret encodes phonemes and whether this representation could account for the human ability to discriminate them. When neural responses were characterized and ordered by spectral tuning and dynamics, perceptually significant features including formant patterns in vowels and place and manner of articulation in consonants, were readily visualized by activity in distinct neural subpopulations. Furthermore, these responses faithfully encoded the similarity between the acoustic features of these phonemes. A simple classifier trained on the neural representation was able to simulate human phoneme confusion when tested with novel exemplars. These results suggest that A1 responses are sufficiently rich to encode and discriminate phoneme classes and that humans and animals may build upon the same general acoustic representations to learn boundaries for categorical and robust sound classification.

Knowledge representation with SOUL

In this paper, we introduce the reader to the field of cognitive network science, that is, the application of network science methods to study human cognition and knowledge structures. Cognitive networks are representations of associative knowledge between concepts in a cognitive system apt at acquiring, storing, processing and producing language, that is, the mental lexicon. In a cognitive network, nodes represent concepts with links expressing relations, such as semantic, syntactic, phonological and visual connections, for example, "canine" and "dog" (nodes) linked by "being synonyms" (link). Hence, cognitive networks represent associative knowledge in mathematical, measurable and quantifiable ways. Can such structure be used to gain insights over cognitive phenomena? We explore this research question by reviewing recent, pioneering key applications and limitations of cognitive networks across visual, auditory, and semantic language processing tasks, either in healthy or clinical populations. We also review applications of cognitive networks modeling language acquisition, reconstructing text content and assessing creativity or personality traits in individuals. Our paper also gently introduces the reader to mathematical notations, definitions and measures about single-layer and multiplex networks as well as hypergraphs. Last but not least, across phonological, semantic and syntactic networks, we guide the reader through relevant psychological frameworks, datasets and software packages that might all aid current and future cognitive network scientists. This article is categorized under: Psychology > Memory Psychology > Theory and Methods Linguistics > Cognitive.

Teaching and Learning Guide for: Pragmatics: From Theory to Experiment and Back Again

Neural tracking in infants – An analytical tool for multisensory social processing in development

Network science provides theoretical, computational, and empirical tools that can be used to understand the structure and function of the human brain in novel ways using simple concepts and mathematical representations. Network neuroscience is a rapidly growing field that is providing considerable insight into human structural connectivity, functional connectivity while at rest, changes in functional networks over time (dynamics), and how these properties differ in clinical populations. In addition, a number of studies have begun to quantify network characteristics in a variety of cognitive processes and provide a context for understanding cognition from a network perspective. In this review, we outline the contributions of network science to cognitive neuroscience. We describe the methodology of network science as applied to the particular case of neuroimaging data and review its uses in investigating a range of cognitive functions including sensory processing, language, emotion, attention, cognitive control, learning, and memory. In conclusion, we discuss current frontiers and the specific challenges that must be overcome to integrate these complementary disciplines of network science and cognitive neuroscience. Increased communication between cognitive neuroscientists and network scientists could lead to significant discoveries under an emerging scientific intersection known as cognitive network neuroscience.

Neural responses to visual stimuli are modulated by spatial and temporal context. For example, in primary visual cortex (V1), responses to an oriented target stimulus will be suppressed when embedded within an oriented surround stimulus. This suppression is orientation-specific, with the largest suppression observed when stimuli in the neuron's classical receptive field and surround are of similar orientation. In human psychological experiments, the tilt illusion and tilt aftereffect demonstrate an effect of context on perceived orientation of a target stimulus. Similar to the neurophysiological data, the strength of these effects is modulated by the orientation difference between the target stimulus and context. It has been hypothesized that the neural mechanism underlying both the tilt illusion and tilt aftereffect involves orientation-tuned inhibition in V1. However, to date there is no direct evidence linking human perception of these illusions with measurements of inhibition from human visual cortex. Here, we measured context-induced suppression of neural responses in human visual cortex using functional magnetic resonance imaging (fMRI). In the same participants, we also measured magnitudes of their tilt illusion and tilt aftereffect. Our data revealed a significant relationship between the magnitude of neural suppression in V1 and size of the tilt illusion and tilt aftereffect. That is, participants who showed stronger blood oxygenation level dependent (BOLD) suppression in V1 also perceived stronger shifts in illusory tilt. This agreement between perception and neural responses in human V1 suggests a shared inhibitory mechanism that mediates both spatial and temporal effects of context in human perception.

This chapter discusses the concepts from the general philosophy of science for those interested in cognitive neuroscience. It covers several major topics in the philosophy of science: scientific explanation and underdetermination, reductionism and levels, and scientific realism. The debates in philosophy of science were largely set in the early-to-mid-1900s by a group of philosophers known as the logical positivists. Logical positivists criticized philosophical questions that could be solved by empirical evidence, so claims about strangers' inner minds could not be a proper subject of science. Logical positivism was largely abandoned by philosophers of science who came to doubt their commitments. Instead of this “layer cake” model, many philosophers now prefer the view of sciences as investigating overlapping subject matter according to their own principles, theories, or methods. Cognitive science, neuroscience, psychology, and related disciplines may all treat different aspects of the same phenomenon or overlapping phenomena.