Cem Uran

Despite numerous studies reporting sensory prediction errors—a key component of predictive processing theories—the nature of the surprise represented in these errors remains largely unknown. Here, we highlight recent studies, which provide evidence that prediction errors, even in early sensory areas, may reflect high-level surprise, offering new insights into the role of predictive processing in the brain beyond classical accounts of redundancy reduction.

Despite numerous studies reporting sensory prediction errors - a key component of predictiveprocessing theories - the nature of the surprise represented in these errors remains largelyunknown. Recent studies provide evidence that prediction errors, even in early sensory areas,may reflect high-level surprise, offering new insights into the role of predictive processing in thebrain beyond classical accounts of redundancy reduction

The cortex generates diverse neural dynamics, ranging from broadband fluctuations to narrowband oscillations at specific frequencies. Here, we investigated whether broadband and oscillatory dynamics play different roles in the encoding and transmission of visual information. We used information-theoretical measures to dissociate neural signals sharing common information (i.e., redundancy) from signals encoding complementary information (i.e., synergy). We analyzed electrocorticography (ECoG) and local field potentials (LFP) in the visual cortex of human and non-human primates (macaque) to investigate the extent to which broadband signals (BB) and narrowband gamma (NBG) oscillations conveyed synergistic or redundant information about images. In both species, the information conveyed by BB signals was highly synergistic within and between visual areas. By contrast, the information carried by NBG was primarily redundant within and between the same visual areas. Finally, the information conveyed by BB signals emerged early after stimulus onset, while NBG sustained information at later time points. These results suggest a potential dual role of BB and NBG cortical dynamics in visual processing, with broadband dynamics supporting nonlinear pattern recognition and oscillations facilitating information maintenance across the cortex.

Theories suggest that cognitive processes like attention have a rhythmic nature, however there is rising controversy surrounding the empirical findings supporting this theory. The controversy centers around the statistical methods employed by studies to test for rhythms in accuracy time courses (ATCs) around a behavioral event. To detect rhythms, previous studies surrogates via shuffling-in-time, and then evaluated if significance occurs in a narrow frequency band (i.e. the presence of a spectral peak). In a recent critique, which challenged the methodological foundations of an entire research field, Brookshire (2022) argued that the shuffling-in-time procedure does not effectively distinguish between periodic and aperiodic processes. Brookshire therefore proposed a new method to test for rhythmic modulations, by creating surrogates via a 1st-order autoregressive process (AR(1) method). Strikingly, Brookshire reports that his AR(1) method does not replicate previously reported evidence for rhythmic modulations in all of 23 analyzed datasets, challenging a significant body of empirical work and a major theory of behavior and cognition. Here, we show that the evidence and arguments provided by Brookshire (2022) against the shuffling-in-time procedure are flawed. Moreover, Brookshire's proposed AR(1) method has a low sensitivity making it irrelevant for the use cases it is designed for. Hence, the reported null-findings by Brookshire (2022) need to be reassessed. We briefly outline a new method (APECS - APEriodiC Surrogate testing) that addresses the shortcomings of previous methods.

What determines the aesthetic appeal of artworks? Recent work suggests that aesthetic appeal can, to some extent, be predicted from a visual artwork’s image features. Yet a large fraction of variance in aesthetic ratings remains unexplained and may relate to individual preferences. We hypothesized that an artwork’s aesthetic appeal depends strongly on self-relevance. In a first study ( N = 33 adults, online replication N = 208), rated aesthetic appeal for real artworks was positively predicted by rated self-relevance. In a second experiment ( N = 45 online), we created synthetic, self-relevant artworks using deep neural networks that transferred the style of existing artworks to photographs. Style transfer was applied to self-relevant photographs selected to reflect participant-specific attributes such as autobiographical memories. Self-relevant, synthetic artworks were rated as more aesthetically appealing than matched control images, at a level similar to human-made artworks. Thus, self-relevance is a key determinant of aesthetic appeal, independent of artistic skill and image features.

An important question concerning inter-areal communication in the cortex is whether these interactions are synergistic, i.e. brain signals can either share common information (redundancy) or they can encode complementary information that is only available when both signals are considered together (synergy). Here, we dissociated cortical interactions sharing common information from those encoding complementary information during prediction error processing. To this end, we computed co-information, an information-theoretical measure that distinguishes redundant from synergistic information among brain signals. We analyzed auditory and frontal electrocorticography (ECoG) signals in five common awake marmosets performing two distinct auditory oddball tasks and investigated to what extent event-related potentials (ERP) and broadband (BB) dynamics encoded redundant and synergistic information during auditory prediction error processing. In both tasks, we observed multiple patterns of synergy across the entire cortical hierarchy with distinct dynamics. The information conveyed by ERPs and BB signals was highly synergistic even at lower stages of the hierarchy in the auditory cortex, as well as between auditory and frontal regions. Using a brain-constrained neural network, we simulated the spatio-temporal patterns of synergy and redundancy observed in the experimental results and further demonstrated that the emergence of synergy between auditory and frontal regions requires the presence of strong, long-distance, feedback and feedforward connections. These results indicate that the distributed representations of prediction error signals across the cortical hierarchy can be highly synergistic.

Rhythmic flicker stimulation has gained interest as a treatment for neurodegenerative diseases and a method for frequency tagging neural activity in human EEG/MEG recordings. Yet, little is known about the way in which flicker-induced synchronization propagates across cortical levels and impacts different cell types. Here, we used Neuropixels to simultaneously record from LGN, V1, and CA1 while presenting visual flicker stimuli at different frequencies. LGN neurons showed strong phase locking up to 40Hz, whereas phase locking was substantially weaker in V1 units and absent in CA1 units. Laminar analyses revealed an attenuation of phase locking at 40Hz for each processing stage, with substantially weaker phase locking in the superficial layers of V1. Gamma-rhythmic flicker predominantly entrained fast-spiking interneurons. Optotagging experiments showed that these neurons correspond to either PV+ or narrow-waveform Sst+ neurons. A computational model could explain the observed differences in phase locking based on the neurons’ capacitative low-pass filtering properties. In summary, the propagation of synchronized activity and its effect on distinct cell types strongly depend on its frequency.

Cortical areas are reciprocally coupled via feedforward (FF) and feedback (FB) connections that have distinct laminar profiles. Recurrent interactions between FF and FB streams may underlie context-dependent, flexible processing of sensory stimuli and the formation of predictions. Hierarchical predictive coding theories postulate that the communication of FF sensory input signals and FB predictions uses distinct frequency channels, namely gamma and alpha/beta-frequencies. Our review calls for an update of this dual-frequency theory: Empirical evidence suggests that both gamma and beta rhythms emerge during sensory and motor states characterized by high spatial and temporal predictability, in which case horizontal and top-down FB can explain away FF inputs. By contrast, broadband increases in gamma power for unpredicted stimuli likely result from increased firing rates. Recentevidence further indicates that distinct rhythms emerge in specific networks and are not consistently associated with either FF or FB influences, or with corresponding laminar compartments. Accordingly, we discuss potential functions and mechanisms of rhythms in the local circuit related to efficient coding and synaptic plasticity, rather than the inter-areal communication of FF error and FB prediction signals.

Behavioral studies suggest attention-related performance fluctuates at a 3-8Hz rhythm (Landau and Fries, 2012; Fiebelkorn et al., 2013). Brookshire (2022) argues that these previous studies failed to distinguish periodic from aperiodic processes, which led to the spurious detection of rhythms. The argument is based on simulations of accuracy time courses (ATCs) by autoregressive models of order 1 (AR(1)), which is a known aperiodic process. Brookshire shows that for this aperiodic process, conventional methods often detect spurious peaks in the 3-8Hz range. Here, we argue that stationary stochastic AR(1) processes are invalid models of ATCs in general as ATCs are deterministic, non-stationary signals. When AR(1) realizations are taken as models for deterministic signals, they do not constitute per definition aperiodic processes. Brookshire’s analyses therefore do not conclusively establish that previous methods indeed detected rhythms spuriously. Furthermore, we show that because Brookshire’s method relies on AR(1) model fits, it can have very low sensitivity when non-rhythmic signals are superimposed onto rhythmic signals. Given these limitations, Brookshire’s conclusion that 0/23 reanalyzed data sets provide evidence for rhythmicity needs to be revisited with alternative methods. Here, we argue that an appropriate model of an ATCs is a deterministic signal (akin to an impulse response or event-related potential) with additive white noise due to random sampling of hits and misses. Such a deterministic ATC can be either periodic or aperiodic. We show that when the deterministic ATC is truly aperiodic, detrending and random sampling lead to spurious rhythm detection. This supports the general point that previous studies did not adequately distinguish periodic from aperiodic processes. In fact, we show that previous methods like Landau and Fries (2012) have much higher false positive ratios than is apparent from Brookshire (2022). Our analysis naturally suggest two effective alternative methods, avoiding the use of AR(1) models.