Nägemistaju automaatsete protsesside eksperimentaalne uurimine

Väitekirja elektrooniline versioon ei sisalda publikatsiooneVäitekiri keskendub nägemistaju protsesside eksperimentaalsele uurimisele, mis on suuremal või vähemal määral automaatsed. Uurimistöös on kasutatud erinevaid eksperimentaalseid katseparadigmasid ja katsestiimuleid ning nii käitumuslikke- kui ka ajukuvamismeetodeid. Esimesed kolm empiirilist uurimust käsitlevad liikumisinformatsiooni töötlust, mis on evolutsiooni käigus kujunenud üheks olulisemaks baasprotsessiks nägemistajus. Esmalt huvitas meid, kuidas avastatakse liikuva objekti suunamuutusi, kui samal ajal toimub ka taustal liikumine (Uurimus I). Nägemistaju uurijad on pikka aega arvanud, et liikumist arvutatakse alati mõne välise objekti või tausta suhtes. Meie uurimistulemused ei kinnitanud taolise suhtelise liikumise printsiibi paikapidavust ning toetavad pigem seisukohta, et eesmärkobjekti liikumisinformatsiooni töötlus on automaatne protsess, mis tuvastab silma põhjas toimuvaid nihkeid, ja taustal toimuv seda eriti ei mõjuta. Teise uurimuse tulemused (Uurimus II) näitasid, et nägemissüsteem töötleb väga edukalt ka seda liikumisinformatsiooni, millele vaatleja teadlikult tähelepanu ei pööra. See tähendab, et samal ajal, kui inimene on mõne tähelepanu hõlmava tegevusega ametis, suudab tema aju taustal toimuvaid sündmusi automaatselt registreerida. Igapäevaselt on inimese nägemisväljas alati palju erinevaid objekte, millel on erinevad omadused, mistõttu järgmiseks huvitas meid (Uurimus III), kuidas ühe tunnuse (antud juhul värvimuutuse) töötlemist mõjutab mõne teise tunnusega toimuv (antud juhul liikumiskiiruse) muutus. Näitasime, et objekti liikumine parandas sama objekti värvimuutuse avastamist, mis viitab, et nende kahe omaduse töötlemine ajus ei ole päris eraldiseisev protsess. Samuti tähendab taoline tulemus, et hoolimata ühele tunnusele keskendumisest ei suuda inimene ignoreerida teist tähelepanu tõmbavat tunnust (liikumine), mis viitab taas kord automaatsetele töötlusprotsessidele. Neljas uurimus keskendus emotsionaalsete näoväljenduste töötlusele, kuna need kannavad keskkonnas hakkamasaamiseks vajalikke sotsiaalseid signaale, mistõttu on alust arvata, et nende töötlus on kujunenud suuresti automaatseks protsessiks. Näitasime, et emotsiooni väljendavaid nägusid avastati kiiremini ja kergemini kui neutraalse ilmega nägusid ning et vihane nägu tõmbas rohkem tähelepanu kui rõõmus (Uurimus IV). Väitekirja viimane osa puudutab visuaalset lahknevusnegatiivsust (ingl Visual Mismatch Negativity ehk vMMN), mis näitab aju võimet avastada automaatselt erinevusi enda loodud mudelist ümbritseva keskkonna kohta. Selle automaatse erinevuse avastamise mehhanismi uurimisse andsid oma panuse nii Uurimus II kui Uurimus IV, mis mõlemad pakuvad välja tõendusi vMMN tekkimise kohta eri tingimustel ja katseparadigmades ning ka vajalikke metodoloogilisi täiendusi. Uurimus V on esimene kogu siiani ilmunud temaatilist teadustööd hõlmav ülevaateartikkel ja metaanalüüs visuaalsest lahknevusnegatiivsusest psühhiaatriliste ja neuroloogiliste haiguste korral, mis panustab oluliselt visuaalse lahknevusnegatiivsuse valdkonna arengusse.The research presented and discussed in the thesis is an experimental exploration of processes in visual perception, which all display a considerable amount of automaticity. These processes are targeted from different angles using different experimental paradigms and stimuli, and by measuring both behavioural and brain responses. In the first three empirical studies, the focus is on motion detection that is regarded one of the most basic processes shaped by evolution. Study I investigated how motion information of an object is processed in the presence of background motion. Although it is widely believed that no motion can be perceived without establishing a frame of reference with other objects or motion on the background, our results found no support for relative motion principle. This finding speaks in favour of a simple and automatic process of detecting motion, which is largely insensitive to the surrounding context. Study II shows that the visual system is built to automatically process motion information that is outside of our attentional focus. This means that even if we are concentrating on some task, our brain constantly monitors the surrounding environment. Study III addressed the question of what happens when multiple stimulus qualities (motion and colour) are present and varied, which is the everyday reality of our visual input. We showed that velocity facilitated the detection of colour changes, which suggests that processing motion and colour is not entirely isolated. These results also indicate that it is hard to ignore motion information, and processing it is rather automatically initiated. The fourth empirical study focusses on another example of visual input that is processed in a rather automatic way and carries high survival value – emotional expressions. In Study IV, participants detected emotional facial expressions faster and more easily compared with neutral facial expressions, with a tendency towards more automatic attention to angry faces. In addition, we investigated the emergence of visual mismatch negativity (vMMN) that is one of the most objective and efficient methods for analysing automatic processes in the brain. Study II and Study IV proposed several methodological gains for registering this automatic change-detection mechanism. Study V is an important contribution to the vMMN research field as it is the first comprehensive review and meta-analysis of the vMMN studies in psychiatric and neurological disorders

Is predictability salient? A study of attentional capture by auditory patterns.

In this series of behavioural and electroencephalography (EEG) experiments, we investigate the extent to which repeating patterns of sounds capture attention. Work in the visual domain has revealed attentional capture by statistically predictable stimuli, consistent with predictive coding accounts which suggest that attention is drawn to sensory regularities. Here, stimuli comprised rapid sequences of tone pips, arranged in regular (REG) or random (RAND) patterns. EEG data demonstrate that the brain rapidly recognizes predictable patterns manifested as a rapid increase in responses to REG relative to RAND sequences. This increase is reminiscent of the increase in gain on neural responses to attended stimuli often seen in the neuroimaging literature, and thus consistent with the hypothesis that predictable sequences draw attention. To study potential attentional capture by auditory regularities, we used REG and RAND sequences in two different behavioural tasks designed to reveal effects of attentional capture by regularity. Overall, the pattern of results suggests that regularity does not capture attention.This article is part of the themed issue 'Auditory and visual scene analysis'

Brain Responses Track Patterns in Sound

This thesis uses specifically structured sound sequences, with electroencephalography (EEG) recording and behavioural tasks, to understand how the brain forms and updates a model of the auditory world. Experimental chapters 3-7 address different effects arising from statistical predictability, stimulus repetition and surprise. Stimuli comprised tone sequences, with frequencies varying in regular or random patterns. In Chapter 3, EEG data demonstrate fast recognition of predictable patterns, shown by an increase in responses to regular relative to random sequences. Behavioural experiments investigate attentional capture by stimulus structure, suggesting that regular sequences are easier to ignore. Responses to repetitive stimulation generally exhibit suppression, thought to form a building block of regularity learning. However, the patterns used in this thesis show the opposite effect, where predictable patterns show a strongly enhanced brain response, compared to frequency-matched random sequences. Chapter 4 presents a study which reconciles auditory sequence predictability and repetition in a single paradigm. Results indicate a system for automatic predictability monitoring which is distinct from, but concurrent with, repetition suppression. The brain’s internal model can be investigated via the response to rule violations. Chapters 5 and 6 present behavioural and EEG experiments where violations are inserted in the sequences. Outlier tones within regular sequences evoked a larger response than matched outliers in random sequences. However, this effect was not present when the violation comprised a silent gap. Chapter 7 concerns the ability of the brain to update an existing model. Regular patterns transitioned to a different rule, keeping the frequency content constant. Responses show a period of adjustment to the rule change, followed by a return to tracking the predictability of the sequence. These findings are consistent with the notion that the brain continually maintains a detailed representation of ongoing sensory input and that this representation shapes the processing of incoming information

Error Signals from the Brain: 7th Mismatch Negativity Conference

The 7th Mismatch Negativity Conference presents the state of the art in methods, theory, and application (basic and clinical research) of the MMN (and related error signals of the brain). Moreover, there will be two pre-conference workshops: one on the design of MMN studies and the analysis and interpretation of MMN data, and one on the visual MMN (with 20 presentations). There will be more than 40 presentations on hot topics of MMN grouped into thirteen symposia, and about 130 poster presentations. Keynote lectures by Kimmo Alho, Angela D. Friederici, and Israel Nelken will round off the program by covering topics related to and beyond MMN

Predictive coding in auditory perception: challenges and unresolved questions.

Predictive coding is arguably the currently dominant theoretical framework for the study of perception. It has been employed to explain important auditory perceptual phenomena, and it has inspired theoretical, experimental and computational modelling efforts aimed at describing how the auditory system parses the complex sound input into meaningful units (auditory scene analysis). These efforts have uncovered some vital questions, addressing which could help to further specify predictive coding and clarify some of its basic assumptions. The goal of the current review is to motivate these questions and show how unresolved issues in explaining some auditory phenomena lead to general questions of the theoretical framework. We focus on experimental and computational modelling issues related to sequential grouping in auditory scene analysis (auditory pattern detection and bistable perception), as we believe that this is the research topic where predictive coding has the highest potential for advancing our understanding. In addition to specific questions, our analysis led us to identify three more general questions that require further clarification: (1) What exactly is meant by prediction in predictive coding? (2) What governs which generative models make the predictions? and (3) What (if it exists) is the correlate of perceptual experience within the predictive coding framework

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

Mismatch responses: Probing probabilistic inference in the brain

Sensory signals are governed by statistical regularities and carry valuable information about the unfolding of environmental events. The brain is thought to capitalize on the probabilistic nature of sequential inputs to infer on the underlying (hidden) dynamics driving sensory stimulation. Mis-match responses (MMRs) such as the mismatch negativity (MMN) and the P3 constitute prominent neuronal signatures which are increasingly interpreted as reflecting a mismatch between the current sensory input and the brain’s generative model of incoming stimuli. As such, MMRs might be viewed as signatures of probabilistic inference in the brain and their response dynamics can provide insights into the underlying computational principles. However, given the dominance of the auditory modality in MMR research, the specifics of brain responses to probabilistic sequences across sensory modalities and especially in the somatosensory domain are not well characterized. The work presented here investigates MMRs across the auditory, visual and somatosensory modality by means of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). We designed probabilistic stimulus sequences to elicit and characterize MMRs and employed computational modeling of response dynamics to inspect different aspects of the brain’s generative model of the sensory environment. In the first study, we used a volatile roving stimulus paradigm to elicit somatosensory MMRs and performed single-trial modeling of EEG signals in sensor and source space. Model comparison suggested that responses reflect Bayesian inference based on the estimation of transition probability and limited information integration of the recent past in order to adapt to a changing environment. The results indicated that somatosensory MMRs reflect an initial mismatch between sensory input and model beliefs represented by confidence-corrected surprise (CS) followed by model adjustment dynamics represented by Bayesian surprise (BS). For the second and third study we designed a tri-modal roving stimulus paradigm to delineate modality specific and modality general features of mismatch processing. Computational modeling of EEG signals in study 2 suggested that single-trial dynamics reflect Bayesian inference based on estimation of uni-modal transition probabilities as well as cross-modal conditional dependencies. While early mismatch processing around the MMN tended to reflect CS, later MMRs around the P3 rather reflect BS, in correspondence to the somatosensory study. Finally, the fMRI results of study 3 showed that MMRs are generated by an interaction of modality specific regions in higher order sensory cortices and a modality general fronto-parietal network. Inferior parietal regions in particular were sensitive to expectation violations with respect to the cross-modal contingencies in the stimulus sequences. Overall, our results indicate that MMRs across the senses reflect processes of probabilistic inference in a complex and inherently multi-modal environment.Sensorische Signale sind durch statistische Regularitäten bestimmt und beinhalten wertvolle Informationen über die Entwicklung von Umweltereignissen. Es wird angenommen, dass das Gehirn die Wahrscheinlichkeitseigenschaften sequenzieller Reize nutzt um auf die zugrundeliegenden (verborgenen) Dynamiken zu schließen, welche sensorische Stimulation verursachen. Diskrepanz-Reaktionen ("Mismatch responses"; MMRs) wie die "mismatch negativity" (MMN) und die P3 sind bekannte neuronale Signaturen die vermehrt als Signale einer Diskrepanz zwischen der momentanen sensorischen Einspeisung und dem generativen Modell, welches das Gehirn von den eingehenden Reizen erstellt angesehen werden. Als solche können MMRs als Signaturen von wahrscheinlichkeitsbasierter Inferenz im Gehirn betrachtet werden und ihre Reaktionsdynamiken können Einblicke in die zugrundeliegenden komputationalen Prinzipien geben. Angesichts der Dominanz der auditorischen Modalität in der MMR-Forschung, sind allerdings die spezifischen Eigenschaften von Hirn-Reaktionen auf Wahrscheinlichkeitssequenzen über sensorische Modalitäten hinweg und vor allem in der somatosensorischen Modalität nicht gut charakterisiert. Die hier vorgestellte Arbeit untersucht MMRs über die auditorische, visuelle und somatosensorische Modalität hinweg anhand von Elektroenzephalographie (EEG) und funktioneller Magnetresonanztomographie (fMRT). Wir gestalteten wahrscheinlichkeitsbasierte Reizsequenzen, um MMRs auszulösen und zu charakterisieren und verwendeten komputationale Modellierung der Reaktionsdynamiken, um verschiedene Aspekte des generativen Modells des Gehirns von der sensorischen Umwelt zu untersuchen. In der ersten Studie verwendeten wir ein volatiles "Roving-Stimulus"-Paradigma, um somatosensorische MMRs auszulösen und modellierten die Einzel-Proben der EEG-Signale im sensorischen und Quell-Raum. Modellvergleiche legten nahe, dass die Reaktionen Bayes’sche Inferenz abbilden, basierend auf der Schätzung von Transitionswahrscheinlichkeiten und limitierter Integration von Information der jüngsten Vergangenheit, welche eine Anpassung an Umweltänderungen ermöglicht. Die Ergebnisse legen nahe, dass somatosen-sorische MMRs eine initiale Diskrepanz zwischen sensorischer Einspeisung und Modellüberzeugung reflektieren welche durch "confidence-corrected surprise" (CS) repräsentiert ist, gefolgt von Modelanpassungsdynamiken repräsentiert von "Bayesian surprise" (BS). Für die zweite und dritte Studie haben wir ein Tri-Modales "Roving-Stimulus"-Paradigma gestaltet, um modalitätsspezifische und modalitätsübergreifende Eigenschaften von Diskrepanzprozessierung zu umreißen. Komputationale Modellierung von EEG-Signalen in Studie 2 legte nahe, dass Einzel-Proben Dynamiken Bayes’sche Inferenz abbilden, basierend auf der Schätzung von unimodalen Transitionswahrscheinlichkeiten sowie modalitätsübergreifenden bedingten Abhängigkeiten. Während frühe Diskrepanzprozessierung um die MMN dazu tendierten CS zu reflektieren, so reflektierten spätere MMRs um die P3 eher BS, in Übereinstimmung mit der somatosensorischen Studie. Abschließend zeigten die fMRT-Ergebnisse der Studie 3 dass MMRs durch eine Interaktion von modalitätsspezifischen Regionen in sensorischen Kortizes höherer Ordnung mit einem modalitätsübergreifenden fronto-parietalen Netzwerk generiert werden. Inferior parietale Regionen im Speziellen waren sensitiv gegenüber Erwartungsverstoß in Bezug auf die modalitätsübergreifenden Wahrscheinlichkeiten in den Reizsequenzen. Insgesamt weisen unsere Ergebnisse darauf hin, dass MMRs über die Sinne hinweg Prozesse von wahrscheinlichkeitsbasierter Inferenz in einer komplexen und inhärent multi-modalen Umwelt darstellen

Different roles of similarity and predictability in auditory stream segregation

Sound sources often emit trains of discrete sounds, such as a series of footsteps. Previously, two dif¬ferent principles have been suggested for how the human auditory system binds discrete sounds to¬gether into perceptual units. The feature similarity principle is based on linking sounds with similar characteristics over time. The predictability principle is based on linking sounds that follow each other in a predictable manner. The present study compared the effects of these two principles. Participants were presented with tone sequences and instructed to continuously indicate whether they perceived a single coherent sequence or two concurrent streams of sound. We investigated the influence of separate manipulations of similarity and predictability on these perceptual reports. Both grouping principles affected perception of the tone sequences, albeit with different characteristics. In particular, results suggest that whereas predictability is only analyzed for the currently perceived sound organization, feature similarity is also analyzed for alternative groupings of sound. Moreover, changing similarity or predictability within an ongoing sound sequence led to markedly different dynamic effects. Taken together, these results provide evidence for different roles of similarity and predictability in auditory scene analysis, suggesting that forming auditory stream representations and competition between alter¬natives rely on partly different processes

EEG mismatch responses in a multimodal roving stimulus paradigm provide evidence for probabilistic inference across audition, somatosensation, and vision

The human brain is constantly subjected to a multimodal stream of probabilistic sensory inputs. Electroencephalography (EEG) signatures, such as the mismatch negativity (MMN) and the P3, can give valuable insight into neuronal probabilistic inference. Although reported for different modalities, mismatch responses have largely been studied in isolation, with a strong focus on the auditory MMN. To investigate the extent to which early and late mismatch responses across modalities represent comparable signatures of uni- and cross-modal probabilistic inference in the hierarchically structured cortex, we recorded EEG from 32 participants undergoing a novel tri-modal roving stimulus paradigm. The employed sequences consisted of high and low intensity stimuli in the auditory, somatosensory and visual modalities and were governed by unimodal transition probabilities and cross-modal conditional dependencies. We found modality specific signatures of MMN (~100–200 ms) in all three modalities, which were source localized to the respective sensory cortices and shared right lateralized prefrontal sources. Additionally, we identified a cross-modal signature of mismatch processing in the P3a time range (~300–350 ms), for which a common network with frontal dominance was found. Across modalities, the mismatch responses showed highly comparable parametric effects of stimulus train length, which were driven by standard and deviant response modulations in opposite directions. Strikingly, P3a responses across modalities were increased for mispredicted stimuli with low cross-modal conditional probability, suggesting sensitivity to multimodal (global) predictive sequence properties. Finally, model comparisons indicated that the observed single trial dynamics were best captured by Bayesian learning models tracking unimodal stimulus transitions as well as cross-modal conditional dependencies