Great expectations: Is there evidence for predictive coding in auditory cortex?

Predictive coding is possibly one of the most influential, comprehensive, and controversial theories of neural function. Whilst proponents praise its explanatory potential, critics object that key tenets of the theory are untested or even untestable. The present article critically examines existing evidence for predictive coding in the auditory modality. Specifically, we identify five key assumptions of the theory and evaluate each in the light of animal, human and modelling studies of auditory pattern processing. For the first two assumptions - that neural responses are shaped by expectations and that these expectations are hierarchically organised - animal and human studies provide compelling evidence. The anticipatory, predictive nature of these expectations also enjoys empirical support, especially from studies on unexpected stimulus omission. However, for the existence of separate error and prediction neurons, a key assumption of the theory, evidence is lacking. More work exists on the proposed oscillatory signatures of predictive coding, and on the relation between attention and precision. However, results on these latter two assumptions are mixed or contradictory. Looking to the future, more collaboration between human and animal studies, aided by model-based analyses will be needed to test specific assumptions and implementations of predictive coding - and, as such, help determine whether this popular grand theory can fulfil its expectations

Auditory associative learning and its neural correlates in the auditory midbrain

Interpreting the meaning of environmental stimuli to generate optimal behavioral responses is essential for survival. Simply sensing a sound, without accessing prior knowledge in the brain, will not benefit behavior. How sensation and memory interact to form behavior is one of the fundamental questions in the field of neuroscience. In this thesis, I have addressed this question from two perspectives: I investigated the behavioral outcome of this interaction using discrimination, and the circuit underlying this interaction using electrophysiological recordings in the behaving mouse. Behaviorally, we found that the physical difference between to-be-discriminated sounds, had a constraining effect on discrimination. This effect occurred even though physical differences were significantly larger than reported discrimination limens, thus reflecting a high overlap between the memory traces of the relevant stimuli. The results suggest a strong role of pre-wired tonotopic organization and the involvement of peripheral stations with wider tuning (Ehret and Merzenich, 1985; Taberner and Liberman, 2005). To further understand the influence of sensation on behavior, we tested the interaction between sound features with generalization. Using sounds that differed in two dimensions, we found that bi-dimensional generalization can be either biased towards a single dimension or an integration of both. Whether it was one or the other depended on the two dimensions used. As the first convergence station in the auditory system (Casseday et al., 2002), the role of the inferior colliculus in encoding behavioral relevant information is not well understood. Recording from freely behaving mouse, we found task engagement modulated neural activity in the IC in a stimulus-specific manner. Our lab found previously that relevant sound exposure induced enhancement in neural activity and shifts in tonal representation in the IC (Cruces-Solís et al., 2018). As a continuation, we found that movement-sound association is essential for this plasticity. Furthermore, recording in freely behaving mice also found that this association modulated the ongoing LFP in the IC, suggesting a new role of IC in filtering movement-related acoustic stimuli. To conclude, our results support the view that the IC is not simply an auditory structure that relays auditory information into the cortex, but plays important role in interpreting the meaning of the sound. The new role of IC in encoding movement-related information suggests that the filtering function of the auditory system starts already in subcortical stages of the auditory pathway

Neuronal adaptation, novelty detection and regularity encoding in audition

The ability to detect unexpected stimuli in the acoustic environment and determine their behavioral relevance to plan an appropriate reaction is critical for survival. This perspective article brings together several viewpoints and discusses current advances in understanding the mechanisms the auditory system implements to extract relevant information from incoming inputs and to identify unexpected events. This extraordinary sensitivity relies on the capacity to codify acoustic regularities, and is based on encoding properties that are present as early as the auditory midbrain. We review state-of-the-art studies on the processing of stimulus changes using non-invasive methods to record the summed electrical potentials in humans, and those that examine single-neuron responses in animal models. Human data will be based on mismatch negativity (MMN) and enhanced middle latency responses (MLR). Animal data will be based on the activity of single neurons at the cortical and subcortical levels, relating selective responses to novel stimuli to the MMN and to stimulus-specific neural adaptation (SSA). Theoretical models of the neural mechanisms that could create SSA and novelty responses will also be discussed

Timing predictability enhances regularity encoding in the human subcortical auditory pathway

The encoding of temporal regularities is a critical property of the auditory system, as short-term neural representations of environmental statistics serve to auditory object formation and detection of potentially relevant novel stimuli. A putative neural mechanism underlying regularity encoding is repetition suppression, the reduction of neural activity to repeated stimulation. Although repetitive stimulation per se has shown to reduce auditory neural activity in animal cortical and subcortical levels and in the human cerebral cortex, other factors such as timing may influence the encoding of statistical regularities. This study was set out to investigate whether temporal predictability in the ongoing auditory input modulates repetition suppression in subcortical stages of the auditory processing hierarchy. Human auditory frequency-following responses (FFR) were recorded to a repeating consonant-vowel stimuli (/wa/) delivered in temporally predictable and unpredictable conditions. FFR amplitude was attenuated by repetition independently of temporal predictability, yet we observed an accentuated suppression when the incoming stimulation was temporally predictable. These findings support the view that regularity encoding spans across the auditory hierarchy and point to temporal predictability as a modulatory factor of regularity encoding in early stages of the auditory pathway

Prediction-related neural response alterations in the ventral visual stream

Theories of predictive coding (PC; Rao & Ballard, 1999) have dominated neurocognitive research in explaining thought and perception processes in various domains. The basic principle is that perception relies not only on bottom-up processing of sensory input but also on top-down predictions. The current thesis describes several neuronal response alterations in cortical visual areas measured with neuroimaging methods. The so-called repetition suppression (RS) effect was connected to predictive coding as repetitions make stimuli more expected, which results in a smaller prediction error and therefore attenuated neuronal activity. Still, it is questioned whether RS reflects the PE or is a local process by neuronal populations that occurs without top-down influences (Grill-Spector et al., 2006). Another often investigated effect is the reduced neuronal response to expected or predicted visual input called expectation suppression (ES). A considerable body of research on contextual response changes, such as RS and ES, relates to the visual system and the face-processing network in particular. Overall, we demonstrate the importance of stimulus predictability for studies using RS to uncover expectancy-related effects. Furthermore, we suggest that the influence of sensory precision on measures of RS and ES needs more attention in future research. Concerning the stimulus material in the presented studies - unfamiliar, visually familiar, and famous familiar faces - we also emphasize the importance of thoroughly considering the characteristics of faces in terms of prior belief and sensory input precision and predictability when using them for testing prediction-related effects

Adjudicating between local and global architectures of predictive processing in the subcortical auditory pathway

Predictive processing, a leading theoretical framework for sensory processing, suggests that the brain constantly generates predictions on the sensory world and that perception emerges from the comparison between these predictions and the actual sensory input. This requires two distinct neural elements: generative units, which encode the model of the sensory world; and prediction error units, which compare these predictions against the sensory input. Although predictive processing is generally portrayed as a theory of cerebral cortex function, animal and human studies over the last decade have robustly shown the ubiquitous presence of prediction error responses in several nuclei of the auditory, somatosensory, and visual subcortical pathways. In the auditory modality, prediction error is typically elicited using so-called oddball paradigms, where sequences of repeated pure tones with the same pitch are at unpredictable intervals substituted by a tone of deviant frequency. Repeated sounds become predictable promptly and elicit decreasing prediction error; deviant tones break these predictions and elicit large prediction errors. The simplicity of the rules inducing predictability make oddball paradigms agnostic about the origin of the predictions. Here, we introduce two possible models of the organizational topology of the predictive processing auditory network: (1) the global view, that assumes that predictions on the sensory input are generated at high-order levels of the cerebral cortex and transmitted in a cascade of generative models to the subcortical sensory pathways; and (2) the local view, that assumes that independent local models, computed using local information, are used to perform predictions at each processing stage. In the global view information encoding is optimized globally but biases sensory representations along the entire brain according to the subjective views of the observer. The local view results in a diminished coding efficiency, but guarantees in return a robust encoding of the features of sensory input at each processing stage. Although most experimental results to-date are ambiguous in this respect, recent evidence favors the global model

Theory of Mind: A Neural Prediction Problem

Predictive coding posits that neural systems make forward-looking predictions about incoming information. Neural signals contain information not about the currently perceived stimulus, but about the difference between the observed and the predicted stimulus. We propose to extend the predictive coding framework from high-level sensory processing to the more abstract domain of theory of mind; that is, to inferences about others’ goals, thoughts, and personalities. We review evidence that, across brain regions, neural responses to depictions of human behavior, from biological motion to trait descriptions, exhibit a key signature of predictive coding: reduced activity to predictable stimuli. We discuss how future experiments could distinguish predictive coding from alternative explanations of this response profile. This framework may provide an important new window on the neural computations underlying theory of mind

Cortico-hippocampal activations for high entropy visual stimulus: an fMRI perspective

We perceive the environment around us in order to act upon it. To gain the desirable outcome effectively, we not only need the incoming information to be processed efficiently but we also need to know how reliable this information is. How this uncertainty is extracted from the visual input and how is it represented in the brain are still open questions. The hippocampus reacts to different measures of uncertainty. Because it is strongly connected to different cortical and subcortical regions, the hippocampus has the resources to communicate such information to other brain regions involved in visual processing and other cognitive processes. In this thesis, we investigate the aspects of uncertainty to which the hippocampus reacts. Is it the uncertainty in the ongoing recognition attempt of a temporally unfolding stimulus or is it the low-level spatiotemporal entropy? To answer this question, we used a dynamic visual stimulus with varying spatial and spatiotemporal entropy. We used well-structured virtual tunnel videos and the corresponding phase-scrambled videos with matching local luminance and contrast per frame. We also included pixel scrambled videos with high spatial and spatiotemporal entropy in our stimulus set. Brain responses (fMRI images) from the participants were recorded while they watched these videos and performed an engaging but cognitively independent task. Using the General Linear Model (GLM), we modeled the brain responses corresponding to different video types and found that the early visual cortex and the hippocampus had a stronger response to videos with higher spatiotemporal entropy. Using independent component analysis, we further investigated which underlying networks were recruited in processing high entropy visual information. We also discovered how these networks might influence each other. We found two cortico-hippocampal networks involved in processing our stimulus videos. While one of them represented a general primary visual processing network, the other was activated strongly by the high entropy videos and deactivated by the well-structured virtual tunnel videos. We also found a hierarchy in the processing stream with information flowing from less stimulus-specific to more stimulus-specific networks

Theory of Mind: A Neural Prediction Problem

Predictive coding posits that neural systems make forward-looking predictions about incoming information. Neural signals contain information not about the currently perceived stimulus, but about the difference between the observed and the predicted stimulus. We propose to extend the predictive coding framework from high-level sensory processing to the more abstract domain of theory of mind; that is, to inferences about others' goals, thoughts, and personalities. We review evidence that, across brain regions, neural responses to depictions of human behavior, from biological motion to trait descriptions, exhibit a key signature of predictive coding: reduced activity to predictable stimuli. We discuss how future experiments could distinguish predictive coding from alternative explanations of this response profile. This framework may provide an important new window on the neural computations underlying theory of mind.National Science Foundation (U.S.) (Award 0645960)National Science Foundation (U.S.) (Award 095518)National Institutes of Health (U.S.) (Grant 1R01 MH096914-01A1