Neurobiological Signatures of Auditory False Perception and Phantom Perception as a Consequence of Sensory Prediction Errors

In this study, we hypothesized that top-down sensory prediction error due to peripheral hearing loss might influence sensorimotor integration using the efference copy (EC) signals as functional connections between auditory and motor brain areas. Using neurophysiological methods, we demonstrated that the auditory responses to self-generated sound were not suppressed in a group of patients with tinnitus accompanied by significant hearing impairment and in a schizophrenia group. However, the response was attenuated in a group with tinnitus accompanied by mild hearing impairment, similar to a healthy control group. The bias of attentional networks to self-generated sound was also observed in the subjects with tinnitus with significant hearing impairment compared to those with mild hearing impairment and healthy subjects, but it did not reach the notable disintegration found in those in the schizophrenia group. Even though the present study had significant constraints in that we did not include hearing loss subjects without tinnitus, these results might suggest that auditory deafferentation (hearing loss) may influence sensorimotor integration process using EC signals. However, the impaired sensorimotor integration in subjects with tinnitus with significant hearing impairment may have resulted from aberrant auditory signals due to sensory loss, not fundamental deficits in the reafference system, as the auditory attention network to self-generated sound is relatively well preserved in these subjects.

Evaluation of a Simple Clinical Language Paradigm With Respect to Sensory Independency, Functional Asymmetry, and Effective Connectivity

The present study replicates a known visual language paradigm, and extends it to a paradigm that is independent from the sensory modality of the stimuli and, hence, could be administered either visually or aurally, such that both patients with limited sight or hearing could be examined. The stimuli were simple sentences, but required the subject not only to understand the content of the sentence but also to formulate a response that had a semantic relation to the content of the presented sentence. Thereby, this paradigm does not only test perception of the stimuli, but also to some extend sentence and semantic processing, and covert speech production within one task. When the sensory base-line condition was subtracted, both the auditory and visual version of the paradigm demonstrated a broadly overlapping and asymmetric network, comprising distinct areas of the left posterior temporal lobe, left inferior frontal areas, left precentral gyrus, and supplementary motor area. The consistency of activations and their asymmetry was evaluated with a conjunction analysis, probability maps, and intraclass correlation coefficients (ICC). This underlying network was further analyzed with dynamic causal modeling (DCM) to explore whether not only the same brain areas were involved, but also the network structure and information flow were the same between the sensory modalities. In conclusion, the paradigm reliably activated the most central parts of the speech and language network with a great consistency across subjects, and independently of whether the stimuli were administered aurally or visually. However, there was individual variability in the degree of functional asymmetry between the two sensory conditions.

Biases and Variability from Costly Bayesian Inference

When humans infer underlying probabilities from stochastic observations, they exhibit biases and variability that cannot be explained on the basis of sound, Bayesian manipulations of probability. This is especially salient when beliefs are updated as a function of sequential observations. We introduce a theoretical framework in which biases and variability emerge from a trade-off between Bayesian inference and the cognitive cost of carrying out probabilistic computations. We consider two forms of the cost: a precision cost and an unpredictability cost; these penalize beliefs that are less entropic and less deterministic, respectively. We apply our framework to the case of a Bernoulli variable: the bias of a coin is inferred from a sequence of coin flips. Theoretical predictions are qualitatively different depending on the form of the cost. A precision cost induces overestimation of small probabilities, on average, and a limited memory of past observations, and, consequently, a fluctuating bias. An unpredictability cost induces underestimation of small probabilities and a fixed bias that remains appreciable even for nearly unbiased observations. The case of a fair (equiprobable) coin, however, is singular, with non-trivial and slow fluctuations in the inferred bias. The proposed framework of costly Bayesian inference illustrates the richness of a 'resource-rational' (or 'bounded-rational') picture of seemingly irrational human cognition.

Functions of Memory Across Saccadic Eye Movements

Several times per second, humans make rapid eye movements called saccades which redirect their gaze to sample new regions of external space. Saccades present unique challenges to both perceptual and motor systems. During the movement, the visual input is smeared across the retina and severely degraded. Once completed, the projection of the world onto the retina has undergone a large-scale spatial transformation. The vector of this transformation, and the new orientation of the eye in the external world, is uncertain. Memory for the pre-saccadic visual input is thought to play a central role in compensating for the disruption caused by saccades. Here, we review evidence that memory contributes to (1) detecting and identifying changes in the world that occur during a saccade, (2) bridging the gap in input so that visual processing does not have to start anew, and (3) correcting saccade errors and recalibrating the oculomotor system to ensure accuracy of future saccades. We argue that visual working memory (VWM) is the most likely candidate system to underlie these behaviours and assess the consequences of VWM's strict resource limitations for transsaccadic processing. We conclude that a full understanding of these processes will require progress on broader unsolved problems in psychology and neuroscience, in particular how the brain solves the object correspondence problem, to what extent prior beliefs influence visual perception, and how disparate signals arriving with different delays are integrated.

How Do Actions Influence Attitudes? An Inferential Account of the Impact of Action Performance on Stimulus Evaluation

Over the past decade, an increasing number of studies have shown that the performance of specific actions (e.g., approach and avoidance) in response to a stimulus can lead to changes in how that stimulus is evaluated. In contrast to the reigning idea that these effects are mediated by the automatic formation and activation of associations in memory, we describe an inferential account that specifies the inferences underlying the effects and how these inferences are formed. We draw on predictive processing theories to explain the basic processes underlying inferential reasoning and their main characteristics. Our inferential account accommodates past findings, is supported by new findings, and leads to novel predictions as well as concrete recommendations for how action performance can be used to influence real-world behavior.

Predictive Processing and the Representation Wars

Clark has recently suggested that predictive processing advances a theory of neural function with the resources to put an ecumenical end to the "representation wars" of recent cognitive science. In this paper I defend and develop this suggestion. First, I broaden the representation wars to include three foundational challenges to representational cognitive science. Second, I articulate three features of predictive processing's account of internal representation that distinguish it from more orthodox representationalist frameworks. Specifically, I argue that it posits a resemblance-based representational architecture with organism-relative contents that functions in the service of pragmatic success, not veridical representation. Finally, I argue that internal representation so understood is either impervious to the three anti-representationalist challenges I outline or can actively embrace them.

Uncertainty and expectancy deviations require cortico-subcortical cooperation

In a dynamic and uncertain environment it is beneficial to learn the causal structure of the environment in order to minimize uncertainty. This requires determining estimates of probable outcomes, which will guide expectations about incoming information. One key factor in this learning process is to detect whether an unexpected event constitutes a low probability, but valid outcome, or an outright error. The present 7T-fMRI study investigated the role of subcortical structures in regulating this probabilistic inferential learning process. A new task was designed, in which participants learned to calculate the value, and therefore to anticipate the outcome of different visual sequences. Three types of sequences provided unambiguous, ambiguous, and incongruent contextual evidence and each sequence had two outcomes, which differed in their probability of occurrence. We hypothesized that subcortical regions are necessary when expectations are violated, and that their involvement will depend on the nature of the unexpected event. The results show increased dorsomedial striatal and thalamic activation for less probable sequences; in addition, ambiguous sequences also display larger activation in the red nuclei. Incongruent sequences displayed a pattern of subcortical activation restricted to the dorsolateral and the posterior dorsomedial striatum. These results confirm that different subcortical structures regulate uncertainty and expectancy deviations; this is crucial not only for learning to predict events in the environment, but also for flexible cognitive control in general.

Tactile length contraction as Bayesian inference

To perceive, the brain must interpret stimulus-evoked neural activity. This is challenging: The stochastic nature of the neural response renders its interpretation inherently uncertain. Perception would be optimized if the brain used Bayesian inference to interpret inputs in light of expectations derived from experience. Bayesian inference would improve perception on average but cause illusions when stimuli violate expectation. Intriguingly, tactile, auditory, and visual perception are all prone to length contraction illusions, characterized by the dramatic underestimation of the distance between punctate stimuli delivered in rapid succession; the origin of these illusions has been mysterious. We previously proposed that length contraction illusions occur because the brain interprets punctate stimulus sequences using Bayesian inference with a low-velocity expectation. A novel prediction of our Bayesian observer model is that length contraction should intensify if stimuli are made more difficult to localize. Here we report a tactile psychophysical study that tested this prediction. Twenty humans compared two distances on the forearm: a fixed reference distance defined by two taps with 1-s temporal separation and an adjustable comparison distance defined by two taps with temporal separation t ≤ 1 s. We observed significant length contraction: As t was decreased, participants perceived the two distances as equal only when the comparison distance was made progressively greater than the reference distance. Furthermore, the use of weaker taps significantly enhanced participants' length contraction. These findings confirm the model's predictions, supporting the view that the spatiotemporal percept is a best estimate resulting from a Bayesian inference process.

Forward and backward inference in spatial cognition

This paper shows that the various computations underlying spatial cognition can be implemented using statistical inference in a single probabilistic model. Inference is implemented using a common set of 'lower-level' computations involving forward and backward inference over time. For example, to estimate where you are in a known environment, forward inference is used to optimally combine location estimates from path integration with those from sensory input. To decide which way to turn to reach a goal, forward inference is used to compute the likelihood of reaching that goal under each option. To work out which environment you are in, forward inference is used to compute the likelihood of sensory observations under the different hypotheses. For reaching sensory goals that require a chaining together of decisions, forward inference can be used to compute a state trajectory that will lead to that goal, and backward inference to refine the route and estimate control signals that produce the required trajectory. We propose that these computations are reflected in recent findings of pattern replay in the mammalian brain. Specifically, that theta sequences reflect decision making, theta flickering reflects model selection, and remote replay reflects route and motor planning. We also propose a mapping of the above computational processes onto lateral and medial entorhinal cortex and hippocampus.