![sbart unifi sbart unifi](https://formstudelearning.unifi.it/pluginfile.php/29995/mod_book/intro/OneSearch.png)
Previous LCfC studies have shown that a lexically restored context phoneme (e.g., /s/ in Christma#) can alter the perceived place of articulation of a subsequent target phoneme (e.g., the initial phoneme of a stimulus from a tapes‐capes continuum), consistent with the influence of an unambiguous context phoneme in the same position. For example, listeners can leverage lexical knowledge to interpret an ambiguous speech sound, but do such effects reflect direct top‐down influences on perception or merely postperceptual biases? A critical test case in the domain of spoken word recognition is lexically mediated compensation for coarticulation (LCfC). We discuss applications of this approach, including the potential for linking algorithmic/representational theories at different cognitive levels to brain responses through appropriate linking models.Ī long‐standing question in cognitive science is how high‐level knowledge is integrated with sensory input. This allows asking two questions about the predictor variables: 1) Is there a significant neural representation corresponding to this predictor variable? And if so, 2) what are the temporal characteristics of the neural response associated with it? Thus, different predictor variables can be systematically combined and evaluated to jointly model neural processing at multiple hierarchical levels. The deconvolution analysis decomposes the brain signal into distinct responses associated with the different predictor variables by estimating a multivariate temporal response function (mTRF), quantifying the influence of each predictor on brain responses as a function of time(-lags). This is analogous to a multiple regression problem, but with the addition of the time dimension. More generally, we advocate a hypothesis-driven approach in which the experimenter specifies a hierarchy of time-continuous representations that are hypothesized to have contributed to brain responses, and uses those as predictor variables for the electrophysiological signal.
Sbart unifi code#
A companion GitHub repository provides the complete source code for the analysis, from raw data to group level statistics. We demonstrate its use, using continuous speech as a sample paradigm, with a freely available EEG dataset of audiobook listening. Here we introduce the Eelbrain Python toolkit, which makes this kind of analysis easy and accessible. Deconvolution analysis has recently emerged as a promising tool for disentangling electrophysiological brain responses related to such complex models of perception. For instance, during speech perception, humans transform a continuously varying acoustic signal into phonemes, words, and meaning, and these levels all have different, interdependent temporal structures. Parallel models might make the perceptual system more robust, facilitate processing of unexpected inputs, and serve a function in language acquisition.Įven though human experience unfolds continuously in time, it is not strictly linear instead, it entails cascading processes building hierarchical cognitive structures. These results suggest that speech processing recruits both local and unified predictive models in parallel, reconciling previous disparate findings. Neural source localization places the anatomical origins of the different predictive models in non-identical parts of the superior temporal lobes bilaterally, although the more local models tend to be right-lateralized. Local context models, one based on sublexical phoneme sequences, and one based on the phonemes in the current word alone, do uniquely predict some part of early neural responses at the same time, even early responses to phonemes also reflect a unified model that incorporates sentence level constraints to predict upcoming phonemes. Results provide evidence for some aspects of both.
![sbart unifi sbart unifi](https://www.sba.unifi.it/upload/20181219154433647.png)
To distinguish these hypotheses, we tested magnetoencephalography responses to continuous narrative speech for signatures of unified and local predictive models.
![sbart unifi sbart unifi](https://www.sba.unifi.it/upload/sbart.png)
This contrasts with the view that the brain constructs a single unified interpretation of the input, which fully integrates available information across representational hierarchies and predictively modulates even earliest sensory representations. However, previous evidence supports two seemingly contradictory models of how predictive cues are integrated with bottom-up evidence: Classic psycholinguistic paradigms suggest a two-stage model, in which acoustic input is represented fleetingly in a local, context-free manner, but quickly integrated with contextual constraints. It is widely accepted that listeners continuously use the linguistic context to anticipate upcoming concepts, words and phonemes.