The neural systems for perceptual updating

In a constantly changing environment we must adapt to both abrupt and gradual changes to incoming information. Previously, we demonstrated that a distributed network (including the anterior insula and anterior cingulate cortex) was active when participants updated their initial representations (e.g., it's a cat) in a gradually morphing picture task (e.g., now it's a rabbit; Stöttinger et al., 2015). To shed light on whether these activations reflect the proactive decisions to update or perceptual uncertainty, we introduced two additional conditions. By presenting picture morphs twice we controlled for uncertainty in perceptual decision making. Inducing an abrupt shift in a third condition allowed us to differentiate between a proactive decision in uncertainty-driven updating and a reactive decision in surprise-based updating. We replicated our earlier result, showing the robustness of the effect. In addition, we found activation in the anterior insula (bilaterally) and the mid frontal area/ACC in all three conditions, indicative of the importance of these areas in updating of all kinds. When participants were naïve as to the identity of the second object, we found higher activations in the mid-cingulate cortex and cuneus – areas typically associated with task difficulty, in addition to higher activations in the right TPJ most likely reflecting the shift to a new perspective. Activations associated with the proactive decision to update to a new interpretation were found in a network including the dorsal ACC known to be involved in exploration and the endogenous decision to switch to a new interpretation. These findings suggest a general network commonly engaged in all types of perceptual decision making supported by additional networks associated with perceptual uncertainty or updating provoked by either proactive or reactive decision making.FWF Austrian Science Fund, Eliese Richter Program (#V480-B27)Natural Sciences and Engineering Research Council (Discovery Grant #261628-07)Heart and Stroke Foundation of Ontario (#NA 6999)Canadian Institute of Health Research (#219972

Neuropsychologia / The neural systems for perceptual updating

In a constantly changing environment we must adapt to both abrupt and gradual changes to incoming information. Previously, we demonstrated that a distributed network (including the anterior insula and anterior cingulate cortex) was active when participants updated their initial representations (e.g., it's a cat) in a gradually morphing picture task (e.g., now it's a rabbit; Stöttinger et al., 2015). To shed light on whether these activations reflect the proactive decisions to update or perceptual uncertainty, we introduced two additional conditions. By presenting picture morphs twice we controlled for uncertainty in perceptual decision making. Inducing an abrupt shift in a third condition allowed us to differentiate between a proactive decision in uncertainty-driven updating and a reactive decision in surprise-based updating. We replicated our earlier result, showing the robustness of the effect. In addition, we found activation in the anterior insula (bilaterally) and the mid frontal area/ACC in all three conditions, indicative of the importance of these areas in updating of all kinds. When participants were naïve as to the identity of the second object, we found higher activations in the mid-cingulate cortex and cuneus areas typically associated with task difficulty, in addition to higher activations in the right TPJ most likely reflecting the shift to a new perspective. Activations associated with the proactive decision to update to a new interpretation were found in a network including the dorsal ACC known to be involved in exploration and the endogenous decision to switch to a new interpretation. These findings suggest a general network commonly engaged in all types of perceptual decision making supported by additional networks associated with perceptual uncertainty or updating provoked by either proactive or reactive decision making.(VLID)250097

Children struggle beyond preschool-age in a continuous version of the ambiguous figures task

Children until the age of five are only able to reverse an ambiguous figure when they are informed about the second interpretation. In two experiments, we examined whether children’s difficulties would extend to a continuous version of the ambiguous figures task. Children (Experiment 1: 66 3- to 5-year olds; Experiment 2: 54 4- to 9-year olds) and adult controls saw line drawings of animals gradually morph—through well-known ambiguous figures—into other animals. Results show a relatively late developing ability to recognize the target animal, with difficulties extending beyond preschool-age. This delay can neither be explained with improvements in theory of mind, inhibitory control, nor individual differences in eye movements. Even the best achieving children only started to approach adult level performance at the age of 9, suggesting a fundamentally different processing style in children and adults

The mean (and standard deviation) of each parameter for RBDs vs. HCs.

<p>The mean (and standard deviation) of each parameter for RBDs vs. HCs.</p

Frequency of <i>n</i> (length of STM) for HCs and RBDs.

<p>Frequency of <i>n</i> (length of STM) for HCs and RBDs.</p

The average win rate for patient groups versus RELPH.

<p>Each plot represents the average win rate for (A) greedy-RELPH and (B) RELPH, (red lines) against the average win rate of (A) LBD, (B) RBD patients (blue lines) over the last 200 trials in the RPS experiment of Danckert et al. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0094308#pone.0094308-Danckert1" target="_blank">[13]</a>. Error bars represent the standard error of the mean.</p

The average win rate of HCs versus ELPH and RELPH.

<p>Each plot shows the average win rate over the last 200 trials in the RPS experiment of Danckert et al. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0094308#pone.0094308-Danckert1" target="_blank">[13]</a> for HCs versus (A) ELPH, (B) non-greedy ELPH and (C) RELPH. The blue line represents the average win rate of HCs. The red line shows the average win rate for the (A) ELPH, (B) non-greedy ELPH and (C) RELPH. Error bars represent the standard error of the mean.</p

AIC value and Bayes factor computed for each model (ELPH and RELPH) per each group separately.

<p>Bayes factor is calculated as 2ln(k) in which . D in this formula is the observed data which in our case is the participants' sequence of plays.</p

A cortical network that marks the moment when conscious representations are updated.

In order to survive in a complex, noisy and constantly changing environment we need to categorize the world (e.g., Is this food edible or poisonous?) and we need to update our interpretations when things change. How does our brain update when object categories change from one to the next? We investigated the neural correlates associated with this updating process. We used event-related fMRI while people viewed a sequence of images that morphed from one object (e.g., a plane) to another (e.g., a shark). All participants were naïve as to the identity of the second object. The point at which participants \u27saw\u27 the second object was unpredictable and uncontaminated by any dramatic or salient change to the images themselves. The moment when subjective perceptual representations changed activated a circumscribed network including the anterior insula, medial and inferior frontal regions and inferior parietal cortex. In a setting where neither the timing nor nature of the visual transition was predictable, this restricted cortical network signals the time of updating a perceptual representation. The anterior insula and mid-frontal regions (including the ACC) were activated not only at the actual time when change was reported, but also immediately before, suggesting that these areas are also involved in processing alternative options after a mismatch has been detected