A dual role for prediction error in associative learning

Confronted with a rich sensory environment, the brain must learn statistical regularities across sensory domains to construct causal models of the world. Here, we used functional magnetic resonance imaging and dynamic causal modeling (DCM) to furnish neurophysiological evidence that statistical associations are learnt, even when task-irrelevant. Subjects performed an audio-visual target-detection task while being exposed to distractor stimuli. Unknown to them, auditory distractors predicted the presence or absence of subsequent visual distractors. We modeled incidental learning of these associations using a Rescorla--Wagner (RW) model. Activity in primary visual cortex and putamen reflected learning-dependent surprise: these areas responded progressively more to unpredicted, and progressively less to predicted visual stimuli. Critically, this prediction-error response was observed even when the absence of a visual stimulus was surprising. We investigated the underlying mechanism by embedding the RW model into a DCM to show that auditory to visual connectivity changed significantly over time as a function of prediction error. Thus, consistent with predictive coding models of perception, associative learning is mediated by prediction-error dependent changes in connectivity. These results posit a dual role for prediction-error in encoding surprise and driving associative plasticity

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

Reduced prefrontal and temporal processing and recall of high sensation value ads

Public service announcements (PSAs) are non-commercial broadcast ads that are an important part of televised public health campaigns. “Message sensation value” (MSV), a measure of sensory intensity of audio, visual, and content features of an ad, is an important factor in PSA impact. Some communication theories propose that higher message sensation value brings increased attention and cognitive processing, leading to higher ad impact. Others argue that the attention-intensive format could compete with ad\u27s message for cognitive resources and result in reduced processing of PSA content and reduced overall effectiveness. Brain imaging during PSA viewing provides a quantitative surrogate measure of PSA impact and addresses questions of PSA evaluation and design not accessible with traditional subjective and epidemiological methods. We used Blood Oxygenation Level Dependent (BOLD) functional Magnetic Resonance Imaging (fMRI) and recognition memory measures to compare high and low MSV anti-tobacco PSAs and neutral videos. In a short-delay, forced-choice memory test, frames extracted from PSAs were recognized more accurately than frames extracted from the NV. Frames from the low MSV PSAs were better recognized than frames from the high MSV PSAs. The accuracy of recognition of PSA frames was positively correlated with the prefrontal and temporal, and negatively correlated with the occipital cortex activation. The low MSV PSAs were associated with greater prefrontal and temporal activation, than the high MSV PSAs. The high MSV PSAs produced greater activation primarily in the occipital cortex. These findings support the “dual processing” and “limited capacity” theories of communication that postulate a competition between ad\u27s content and format for the viewers\u27 cognitive resources and suggest that the “attention-grabbing” high MSV format could impede the learning and retention of an ad. These findings demonstrate the potential of using neuroimaging in the design and evaluation of mass media public health communications

Change blindness: eradication of gestalt strategies

Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task

Brain Mechanisms Supporting the Modulation of Pain by Mindfulness Meditation

The subjective experience of one’s environment is constructed by interactions among sensory, cognitive, and affective processes. For centuries, meditation has been thought to influence such processes by enabling a nonevaluative representation of sensory events. To better understand how meditation influences the sensory experience, we used arterial spin labeling functional magnetic resonance imaging to assess the neural mechanisms by which mindfulness meditation influences pain in healthy human participants. After 4 d of mindfulness meditation training, meditating in the presence of noxious stimulation significantly reduced pain unpleasantness by 57% and pain intensity ratings by 40% when compared to rest. A two-factor repeated-measures ANOVA was used to identify interactions between meditation and pain-related brain activation. Meditation reduced pain-related activation of the contralateral primary somatosensory cortex. Multiple regression analysis was used to identify brain regions associated with individual differences in the magnitude of meditation-related pain reductions. Meditation-induced reductions in pain intensity ratings were associated with increased activity in the anterior cingulate cortex and anterior insula, areas involved in the cognitive regulation of nociceptive processing. Reductions in pain unpleasantness ratings were associated with orbitofrontal cortex activation, an area implicated in reframing the contextual evaluation of sensory events. Moreover, reductions in pain unpleasantness also were associated with thalamic deactivation, which may reflect a limbic gating mechanism involved in modifying interactions between afferent input and executive-order brain areas. Together, these data indicate that meditation engages multiple brain mechanisms that alter the construction of the subjectively available pain experience from afferent information

A Dual Role for Prediction Error in Associative Learning

Confronted with a rich sensory environment, the brain must learn statistical regularities across sensory domains to construct causal models of the world. Here, we used functional magnetic resonance imaging and dynamic causal modeling (DCM) to furnish neurophysiological evidence that statistical associations are learnt, even when task-irrelevant. Subjects performed an audio-visual target-detection task while being exposed to distractor stimuli. Unknown to them, auditory distractors predicted the presence or absence of subsequent visual distractors. We modeled incidental learning of these associations using a Rescorla-Wagner (RW) model. Activity in primary visual cortex and putamen reflected learning-dependent surprise: these areas responded progressively more to unpredicted, and progressively less to predicted visual stimuli. Critically, this prediction-error response was observed even when the absence of a visual stimulus was surprising. We investigated the underlying mechanism by embedding the RW model into a DCM to show that auditory to visual connectivity changed significantly over time as a function of prediction error. Thus, consistent with predictive coding models of perception, associative learning is mediated by prediction-error dependent changes in connectivity. These results posit a dual role for prediction-error in encoding surprise and driving associative plasticit

Classification of soundscapes of urban public open spaces

It is increasingly acknowledged by landscape architects and urban planners that the soundscape contributes significantly to the perception of urban public open spaces. Describing and classifying this impact, however, remains a challenge. This article presents a hierarchical method for classification that distinguishes between backgrounded and foregrounded, disruptive and supportive, and finally calming and stimulating soundscapes. This four-class classification is applied to a growing collection of immersive audio-visual recordings of sound environments from around the world that could be explored using virtual reality playback. To validate the proposed methodology, an experiment involving 40 participants and 50 soundscape stimuli collected in urban public open spaces worldwide was conducted. The experiment showed that (1) the virtual reality headset reproduction based on affordable spatial audio with 360-degree video recordings was perceived as ecologically valid in terms of realism and immersion; (2) the proposed classification method results in well-separated classes; (3) membership to these classes could be explained by physical parameters, both regarding sound and vision. Moreover, models based on a limited number of acoustical indicators were constructed that could correctly classify a soundscape in each of the four proposed categories, with an accuracy exceeding 88% on an independent dataset