Monitoring Processes in Visual Search Enhanced by Professional Experience: The Case of Orange Quality-Control Workers

Visual search tasks have often been used to investigate how cognitive processes change with expertise. Several studies have shown visual experts' advantages in detecting objects related to their expertise. Here, we tried to extend these findings by investigating whether professional search experience could boost top-down monitoring processes involved in visual search, independently of advantages specific to objects of expertise. To this aim, we recruited a group of quality-control workers employed in citrus farms. Given the specific features of this type of job, we expected that the extensive employment of monitoring mechanisms during orange selection could enhance these mechanisms even in search situations in which orange-related expertise is not suitable. To test this hypothesis, we compared performance of our experimental group and of a well-matched control group on a computerized visual search task. In one block the target was an orange (expertise target) while in the other block the target was a Smurfette doll (neutral target). The a priori hypothesis was to find an advantage for quality-controllers in those situations in which monitoring was especially involved, that is, when deciding the presence/absence of the target required a more extensive inspection of the search array. Results were consistent with our hypothesis. Quality-controllers were faster in those conditions that extensively required monitoring processes, specifically, the Smurfette-present and both target-absent conditions. No differences emerged in the orange-present condition, which resulted to mainly rely on bottom-up processes. These results suggest that top-down processes in visual search can be enhanced through immersive real-life experience beyond visual expertise advantages

Disordered eating, depression, and cognitive vulnerabilities in college women.

This study tests a path model of disordered eating and symptoms of depression derived from the Hopelessness Theory of Depression (Abramson, Metalsky, & Alloy, 1989). The model proposes that cognitive vulnerabilities to depression will be associated with disordered eating behaviors and symptoms of depression in college women. A sample of undergraduate women (n = 181) completed self-report measures assessing disordered eating symptoms and symptoms of depression. Findings revealed that one, but not all, cognitive vulnerability was associated with disordered eating behavior, and that disordered eating behaviors and symptoms of depression are bi-directionally associated. Implications and future research directions are discussed

The effect of emotion intensity on time perception: a study with transcranial random noise stimulation

Emotional facial expressions provide cues for social interactions and emotional events can distort our sense of time. The present study investigates the effect of facial emotional stimuli of anger and sadness on time perception. Moreover, to investigate the causal role of the orbitofrontal cortex (OFC) in emotional recognition, we employed transcranial random noise stimulation (tRNS) over OFC and tested the effect on participants' emotional recognition as well as on time processing. Participants performed a timing task in which they were asked to categorize as "short" or "long" temporal intervals marked by images of people expressing anger, sad or neutral emotional facial expressions. In addition, they were asked to judge if the image presented was of a person expressing anger or sadness. The visual stimuli were facial emotional stimuli indicating anger or sadness with different degrees of intensity at high (80%), medium (60%) and low (40%) intensity, along with neutral emotional face stimuli. In the emotional recognition task, results showed that participants were faster and more accurate when emotional intensity was higher. Moreover, tRNS over OFC interfered with emotion recognition, which is in line with its proposed role in emotion recognition. In the timing task, participants overestimated the duration of angry facial expressions, although neither emotional intensity not OFC stimulation significantly modulated this effect. Conversely, as the emotional intensity increased, participants exhibited a greater tendency to overestimate the duration of sad faces in the sham condition. However, this tendency disappeared with tRNS. Taken together, our results are partially consistent with previous findings showing an overestimation effect of emotionally arousing stimuli, revealing the involvement of OFC in emotional distortions of time, which needs further investigation

The contribution of the supplementary motor area to explicit and implicit timing: A high-definition transcranial Random Noise Stimulation (HD-tRNS) study

It is becoming increasingly accepted that timing tasks, and underlying temporal processes, can be partitioned on the basis of whether they require an explicit or implicit temporal judgement. Most neuroimaging studies of timing associated explicit timing tasks with activation of the supplementary motor area (SMA). However, transcranial magnetic stimulation (TMS) studies perturbing SMA functioning across explicit timing tasks have generally reported null effects, thus failing to causally link SMA to explicit timing. The present study probed the involvement of SMA in both explicit and implicit timing tasks within a single experiment and using HighDefinition transcranial Random Noise Stimulation (HD-tRNS), a previously less used technique in studies of the SMA. Participants performed two tasks that comprised the same stimulus presentation but differed in the received task instructions, which might or might not require explicit temporal judgments. Results showed a significant HD-tRNS-induced shift of perceived durations (i.e., overestimation) in the explicit timing task, whereas there was no modulation of implicit timing by HD-tRNS. Overall, these results provide initial noninvasive brain stimulation evidence on the contribution of the SMA to explicit and implicit timing tasks

Catheters and Infections

Catheters are used for effective drainage of the bladder, either temporally or permanently, in the presence of physiological and anatomical defects or obstruction of the lower urinary tract. Catheters are used for a variety of reasons, as follows, to maintain bladder drainage during and following surgery or epidurals anesthesia for minimizing and prevention of the risk of distension injuries; investigations, for accurate urine output measurement, and measurement of post-micturition residuals; treatments, to relieve urinary retention or for chemotherapy instillation; intractable incontinence, as the final option for containment

Bayesian modeling of temporal expectations in the human brain

The ability to predict when a relevant event might occur is critical to survive in our dynamic and uncertain environment. This cognitive ability, usually referred to as temporal preparation, allows us to prepare temporally optimized responses to forthcoming stimuli by anticipating their timing: from safely crossing a busy road during rush hours, to timing turn taking in a conversation, to catching something in mid-air, are all examples of how important and ubiquitous temporal preparation is in our everyday life (e.g., Correa, 2010; Coull & Nobre, 2008; Nobre, Correa, & Coull, 2007). In laboratory settings, temporal preparation has been traditionally investigated, in its implicit form, through the “variable foreperiod paradigm” (see Coull, 2009; Niemi & Näätänen, 1981, for a review). In such a paradigm, the foreperiod is a time interval of variable duration that separates a warning stimulus and a target stimulus requiring a response. What is usually observed with this paradigm is that response times (RTs) reflect the temporal probability of stimulus onset: RTs decrease with increasing probability. This implies that participants learn to use the information implicitly afforded by the passage of time and that related to the temporal probability of the onset of the target stimulus (i.e., hazard rate; Janssen & Shadlen, 2005). In other words, it seems that they are able to use predictive internal models of event timing in order to optimize behaviour. Despite previous studies have started to investigate which brain areas encode temporal probabilities (i.e., predictive models) to anticipate event onset (e.g., Bueti, Bahrami, Walsh, & Rees, 2010; Cui, Stetson, Montague, & Eagleman, 2009; also see Vallesi et al., 2007), to our knowledge, there is no evidence on how the brain does form and update such predictive models. Based on such premises, the overarching goal of the present PhD project was to pinpoint the neural mechanisms by which predictive models of event timing are dynamically updated. Moreover, given that in real life updating usually occurs in the presence of surprising events (i.e. low probable events under a predictive model), it is challenging to disentangle between updating and surprise (O’Reilly et al, 2013). Therefore, our second and interrelated research goal was to understand whether, and to which extent, it is possible to dissociate between the neural mechanisms specifically involved in updating and those dealing with surprising events that do not require an update of internal models. To accomplish our research goals, we capitalized on both state-of-the-art methodologies [i.e., functional magnetic resonance imaging (fMRI) and electrophysiology (EEG)] and computational modelling. Specifically, we considered the brain like a Bayesian observer. Indeed, Bayesian frameworks are gaining increasing popularity to explain cognitive brain functions (Friston, 2012). In a nutshell, the construction of computational Bayesian models allows us to quantitatively describe temporal expectations in terms of probability distributions and to capture updating using Bayes’ rule. In order to accomplish our goals, the present PhD project is composed of three studies. In the first two studies we implemented a version of the foreperiod paradigm in which participants could predict target onsets by estimating their underlying temporal probability distributions. During the task, these distributions changed, hence requiring participants to update their temporal expectations. Furthermore, a simple manipulation of the colors in which the target were presented (cf., O’Reilly et al., 2013) allowed us to independently vary updating and surprise across trials. Then, we constructed a normative Bayesian learner (a computational model adapted from O’Reilly et al., 2013) in order to obtain an estimate of a participant’s temporal expectations on a trial-by-trial basis. In Study 1, trial-by-trial fMRI data acquired during our foreperiod paradigm were correlated with two information theoretical parameters calculated with reference to our Bayesian model: the Kullbach-Leibler divergence (DKL) and the Shannon’s information (IS). These two measures have been previously used to formally describe belief updating and surprise associated with events under a predictive model, respectively (e.g., Baldi & Itti, 2010; Kolossa, Kopp, & Fingscheidt, 2015; O'Reilly et al., 2013; Strange et al., 2005). Our results showed that the fronto-parietal network and the cingulo-opercular network were differentially involved in the updating of temporal expectations and in dealing with surprising events, respectively. Having successfully validated the use of Bayesian models in our first fMRI study and dissociated between updating and surprise, the next step was to investigate the temporal dynamics of these two processes. Do updating and surprise act on similar or distinct processing stage(s)? What is the time course associated with the two? To address these questions, in Study 2 participants performed our adapted foreperiod task (same task as in Study 1) while their EEG activity was recorded. In this study, we relied on the literature on the P3 (a specific ERP component related to information processing) and the Bayesian brain (e.g., Kopp, 2008; Kopp et al., 2016; Mars et al., 2008; Seer, Lange, Boos, Dengler, & Kopp, 2016). Importantly, however, we also took advantage from the combination of a mass-univariate approach with novel deconvolution methods to explore the entire spatio-temporal pattern of EEG data. This enabled us to extend our analyses beyond the P3 component. Results from study 2 confirmed that surprise and updating can be differentiated also at the electrophysiological level and that updating elicited a more complex pattern than surprise. As regards the P3 in relation to the literature on the Bayesian brain (Kolossa, Fingscheidt, Wessel, & Kopp, 2013; Kolossa et al., 2015; Mars et al., 2008), our findings corroborated the idea that such a component is selectively modulated by surprise and updating. While in Studies 1 and 2, participants were explicitly encouraged to form and update temporal expectations using the target color, in Study 3 we wanted to make a step further by asking whether the use of a more implicit task structure might influence the construction of the predictive internal model. To that aim, during the foreperiod task designed for the third study, participants were not explicitly informed about the presence of the underlying temporal probability distributions from which target onsets were drawn. In this way, we aimed to investigate behavioural and EEG differences in the way participants learnt to form and updated temporal expectations when changes in the underlying distributions were not explicitly signalled. Critically, we again found that surprise and updating could be differentiated. Moreover, coupled with the results from study 2, we isolated two EEG signatures of the inferential process underlying updating of prior temporal expectations, which responded to both explicit and implicit contextual changes. Overall, we believe that the results of the present PhD project will further our understanding of the cognitive processes and neural mechanisms that allow us to optimize our temporal preparation abilities