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

Understanding vulnerability for depression from a cognitive neuroscience perspective: a reappraisal of attentional factors and a new conceptual framework

We propose a framework to understand increases in vulnerability for depression after recurrent episodes that links attention processes and schema activation to negative mood states, by integrating cognitive and neurobiological findings. Depression is characterized by a mood-congruent attentional bias at later stages of information processing. The basic idea of our framework is that decreased activity in prefrontal areas, mediated by the serotonin metabolism which the HPA axis controls, is associated with an impaired attenuation of subcortical regions, resulting in prolonged activation of the amygdala in response to stressors in the environment. Reduced prefrontal control in interaction with depressogenic schemas leads to impaired ability to exert attentional inhibitory control over negative elaborative processes such as rumination, leading in turn to sustained negative affect. These elaborative processes are triggered by the activation of negative schemas after confrontation with stressors. In our framework, attentional impairments are postulated as a crucial process in explaining the increasing vulnerability after depressive episodes, linking cognitive and biological vulnerability factors. We review the empirical data on the biological factors associated with the attentional impairments and detail how they are associated with rumination and mood regulation. The aim of our framework is to stimulate translational research

Attentive monitoring of multiple video streams driven by a Bayesian foraging strategy

In this paper we shall consider the problem of deploying attention to subsets of the video streams for collating the most relevant data and information of interest related to a given task. We formalize this monitoring problem as a foraging problem. We propose a probabilistic framework to model observer's attentive behavior as the behavior of a forager. The forager, moment to moment, focuses its attention on the most informative stream/camera, detects interesting objects or activities, or switches to a more profitable stream. The approach proposed here is suitable to be exploited for multi-stream video summarization. Meanwhile, it can serve as a preliminary step for more sophisticated video surveillance, e.g. activity and behavior analysis. Experimental results achieved on the UCR Videoweb Activities Dataset, a publicly available dataset, are presented to illustrate the utility of the proposed technique.Comment: Accepted to IEEE Transactions on Image Processin

Detect the unexpected: a science for surveillance

Purpose – The purpose of this paper is to outline a strategy for research development focused on addressing the neglected role of visual perception in real life tasks such as policing surveillance and command and control settings. Approach – The scale of surveillance task in modern control room is expanding as technology increases input capacity at an accelerating rate. The authors review recent literature highlighting the difficulties that apply to modern surveillance and give examples of how poor detection of the unexpected can be, and how surprising this deficit can be. Perceptual phenomena such as change blindness are linked to the perceptual processes undertaken by law-enforcement personnel. Findings – A scientific programme is outlined for how detection deficits can best be addressed in the context of a multidisciplinary collaborative agenda between researchers and practitioners. The development of a cognitive research field specifically examining the occurrence of perceptual “failures” provides an opportunity for policing agencies to relate laboratory findings in psychology to their own fields of day-to-day enquiry. Originality/value – The paper shows, with examples, where interdisciplinary research may best be focussed on evaluating practical solutions and on generating useable guidelines on procedure and practice. It also argues that these processes should be investigated in real and simulated context-specific studies to confirm the validity of the findings in these new applied scenarios

Implications and Ramifications of a Sample-Size Approach to Intuition

[...from the chapter] In the present article, we delineate a different approach, which is by no means inconsistent, but largely overlaps with the aforementioned definitions. However, our approach is simpler and refrains from a number of rather strong assumptions to which other conceptions subscribe. Using a simple and straightforward criterion, we define intuition in terms of the size of the sample used in reaching a decision: Judgments and decisions are intuitive to the extent that they rest on small samples.

The choice architect’s trilemma

Critics have long dismissed paternalistic choice architecture as conceptually muddled at best and oxymoronic at worst. In this article, I argue that this criticism remains true despite recent replies to the contrary. Further, I suggest that a similar conceptual criticism also applies to non-paternalistic choice architecture. This is due to a three-way tension between the effectiveness, avoidability, and distinctiveness of each nudge. To illustrate this tension, I provide a novel explanation of the mechanics of nudging and a taxonomy of these interventions. I then argue that choice architects who defend the distinctiveness of nudging according to how it guides our behaviour via our unreflective intuitive reasoning encounter a trilemma because the distinctiveness of nudging hinges on interventions being both avoidable and effective. Choice architects cannot achieve this aim without harnessing both our automatic and reflective systems of thought in tandem. However, this diminishes the distinctive character of nudging by bringing it closer to other traditional policy interventions. This establishes the choice architect’s trilemma: a nudge is likely to be either ineffective, effective via some morally unacceptable means, or effective in a manner that is conceptually indistinct from other evidence-based policy interventions

Visual cognition during real social interaction

Copyright @ 2012 The Authors. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and 85 reproduction in any medium, provided the original author and source are credited. The article was made available through the Brunel University Open Access Publishing Fund.This article has been made available through the Brunel Open Access Publishing Fund.Laboratory studies of social visual cognition often simulate the critical aspects of joint attention by having participants interact with a computer-generated avatar. Recently, there has been a movement toward examining these processes during authentic social interaction. In this review, we will focus on attention to faces, attentional misdirection, and a phenomenon we have termed social inhibition of return (Social IOR), that have revealed aspects of social cognition that were hitherto unknown. We attribute these discoveries to the use of paradigms that allow for more realistic social interactions to take place. We also point to an area that has begun to attract a considerable amount of interest—that of Theory of Mind (ToM) and automatic perspective taking—and suggest that this too might benefit from adopting a similar approach

See no Evil: Challenges of security surveillance and monitoring

While the development of intelligent technologies in security surveillance can augment human capabilities, they do not replace the role of the operator entirely; as such, when developing surveillance support it is critical that limitations to the cognitive system are taken into account. The current article reviews the cognitive challenges associated with the task of a CCTV operator: visual search and cognitive/perceptual overload, attentional failures, vulnerability to distraction, and decision-making in a dynamically evolving environment. While not directly applied to surveillance issues, we suggest that the NSEEV (noticing – salience, effort, expectancy, value) model of attention could provide a useful theoretical basis for understanding the challenges faced in detection and monitoring tasks. Having identified cognitive limitations of the human operator, this review sets out a research agenda for further understanding the cognitive functioning related to surveillance, and highlights the need to consider the human element at the design stage when developing technological solutions to security surveillance

Understanding information diversity in the era of repurposable crowdsourced data

Organizations successfully leverage information technology for the acquisition of knowledge for decision-making through information crowdsourcing, which is gathering information from a group of people about a phenomenon of interest to the crowdsourcer. Information crowdsourcing has been used to drive business insight and scientific research, providing crowdsourcers access to information outside their traditional reach. Crowdsourcers seek high-quality data for their information crowdsourcing projects and require contributors who can provide data that meet predetermined requirements. Crowdsourcers recruit contributors with high levels of relevant knowledge or train contributors to ensure the quality of data they collect. However, when crowdsourced data needs to fit more than a single usage scenario because the requirements of the project changed or the data needs to be repurposed for tasks other than the one(s) for which it was initially collected, the ability of contributors to provide diverse data that can meet multiple requirements is also desirable. In this thesis, I investigate how the domain knowledge a contributor possesses affects the diversity and quality of data they report. Using an experiment in which 84 students randomly assigned to three knowledge conditions reported information about artificial stimuli, I found that explicitly trained contributors provided less diverse data than either implicitly trained or untrained contributors. In addition, I looked at the longitudinal effect of knowledge on the diversity of data reported by contributors. Using review data from Amazon.com and organism sighting data from NLNature.com (a citizen science data crowdsourcing platform), I studied the impact of knowledge on the diversity and quality of crowdsourced data. The results show that experience reduced the diversity and usefulness of contributed data. The study provides insights for crowdsourcers in industry and academia on how to manage and utilize their crowds effectively to collect high-quality reusable data