scholarly journals A Distinct Role of the Temporal-Parietal Junction in Predicting Socially Guided Decisions

Science ◽  
2012 ◽  
Vol 337 (6090) ◽  
pp. 109-111 ◽  
Author(s):  
R. McKell Carter ◽  
Daniel L. Bowling ◽  
Crystal Reeck ◽  
Scott A. Huettel

To make adaptive decisions in a social context, humans must identify relevant agents in the environment, infer their underlying strategies and motivations, and predict their upcoming actions. We used functional magnetic resonance imaging, in conjunction with combinatorial multivariate pattern analysis, to predict human participants’ subsequent decisions in an incentive-compatible poker game. We found that signals from the temporal-parietal junction provided unique information about the nature of the upcoming decision, and that information was specific to decisions against agents who were both social and relevant for future behavior.

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
David Wisniewski ◽  
Birte Forstmann ◽  
Marcel Brass

AbstractValue-based decision-making is ubiquitous in every-day life, and critically depends on the contingency between choices and their outcomes. Only if outcomes are contingent on our choices can we make meaningful value-based decisions. Here, we investigate the effect of outcome contingency on the neural coding of rewards and tasks. Participants performed a reversal-learning paradigm in which reward outcomes were contingent on trial-by-trial choices, and performed a ‘free choice’ paradigm in which rewards were random and not contingent on choices. We hypothesized that contingent outcomes enhance the neural coding of rewards and tasks, which was tested using multivariate pattern analysis of fMRI data. Reward outcomes were encoded in a large network including the striatum, dmPFC and parietal cortex, and these representations were indeed amplified for contingent rewards. Tasks were encoded in the dmPFC at the time of decision-making, and in parietal cortex in a subsequent maintenance phase. We found no evidence for contingency-dependent modulations of task signals, demonstrating highly similar coding across contingency conditions. Our findings suggest selective effects of contingency on reward coding only, and further highlight the role of dmPFC and parietal cortex in value-based decision-making, as these were the only regions strongly involved in both reward and task coding.


2020 ◽  
Vol 123 (1) ◽  
pp. 167-177 ◽  
Author(s):  
Quentin Moreau ◽  
Eleonora Parrotta ◽  
Vanessa Era ◽  
Maria Luisa Martelli ◽  
Matteo Candidi

Neuroimaging and EEG studies have shown that passive observation of the full body and of specific body parts is associated with 1) activity of an occipito-temporal region named the extrastriate body area (EBA), 2) amplitude modulations of a specific posterior event-related potential (ERP) component (N1/N190), and 3) a theta-band (4–7 Hz) synchronization recorded from occipito-temporal electrodes compatible with the location of EBA. To characterize the functional role of the occipito-temporal theta-band increase during the processing of body-part stimuli, we recorded EEG from healthy participants while they were engaged in an identification task (match-to-sample) of images of hands and nonbody control images (leaves). In addition to confirming that occipito-temporal electrodes show a larger N1 for hand images compared with control stimuli, cluster-based analysis revealed an occipito-temporal cluster showing an increased theta power when hands are presented (compared with leaves) and show that this theta increase is higher for identified hands compared with nonidentified ones while not being significantly different between not identified nonhand stimuli. Finally, single trial multivariate pattern analysis revealed that time-frequency modulation in the theta band is a better marker for classifying the identification of hand images than the ERP modulation. The present results support the notion that theta activity over the occipito-temporal cortex is an informative marker of hand visual processing and may reflect the activity of a network coding for stimulus identity. NEW & NOTEWORTHY Hands provide crucial information regarding the identity of others, which is a key information for social processes. We recorded EEG activity of healthy participants during the visual identification of hand images. The combination of univariate and multivariate pattern analysis in time- and time-frequency domain highlights the functional role of theta (4–7 Hz) activity over visual areas during hand identification and emphasizes the robustness of this neuromarker in occipito-temporal visual processing dynamics.


2016 ◽  
Vol 28 (9) ◽  
pp. 1345-1357 ◽  
Author(s):  
Merim Bilalić

The fusiform face area (FFA) is considered to be a highly specialized brain module because of its central importance for face perception. However, many researchers claim that the FFA is a general visual expertise module that distinguishes between individual examples within a single category. Here, I circumvent the shortcomings of some previous studies on the FFA controversy by using chess stimuli, which do not visually resemble faces, together with more sensitive methods of analysis such as multivariate pattern analysis. I also extend the previous research by presenting chess positions, complex scenes with multiple objects, and their interrelations to chess experts and novices as well as isolated chess objects. The first experiment demonstrates that chess expertise modulated the FFA activation when chess positions were presented. In contrast, single chess objects did not produce different activation patterns among experts and novices even when the multivariate pattern analysis was used. The second experiment focused on the single chess objects and featured an explicit task of identifying the chess objects but failed to demonstrate expertise effects in the FFA. The experiments provide support for the general expertise view of the FFA function but also extend the scope of our understanding about the function of the FFA. The FFA does not merely distinguish between different exemplars within the same category of stimuli. More likely, it parses complex multiobject stimuli that contain numerous functional and spatial relations.


2021 ◽  
Vol 15 ◽  
Author(s):  
Sebastian P. H. Speer ◽  
Ale Smidts ◽  
Maarten A. S. Boksem

There is a long-standing debate regarding the cognitive nature of (dis)honesty: Is honesty an automatic response or does it require willpower in the form of cognitive control in order to override an automatic dishonest response. In a recent study (Speer et al., 2020), we proposed a reconciliation of these opposing views by showing that activity in areas associated with cognitive control, particularly the inferior frontal gyrus (IFG), helped dishonest participants to be honest, whereas it enabled cheating for honest participants. These findings suggest that cognitive control is not needed to be honest or dishonest per se but that it depends on an individual’s moral default. However, while our findings provided insights into the role of cognitive control in overriding a moral default, they did not reveal whether overriding honest default behavior (non-habitual dishonesty) is the same as overriding dishonest default behavior (non-habitual honesty) at the neural level. This speaks to the question as to whether cognitive control mechanisms are domain-general or may be context specific. To address this, we applied multivariate pattern analysis to compare neural patterns of non-habitual honesty to non-habitual dishonesty. We found that these choices are differently encoded in the IFG, suggesting that engaging cognitive control to follow the norm (that cheating is wrong) fundamentally differs from applying control to violate this norm.


2020 ◽  
Author(s):  
Song Qi ◽  
Logan Cross ◽  
Toby Wise ◽  
Xin Sui ◽  
John O’Doherty ◽  
...  

Humans, like many other animals, pre-empt danger by moving to locations that maximize their success at escaping future threats. We test the idea that spatial margin of safety (MOS) decisions, a form of pre-emptive avoidance, results in participants placing themselves closer to safer locations when facing more unpredictable threats. Using multivariate pattern analysis on fMRI data collected while subjects engaged in MOS decisions with varying attack location predictability, we show that while the hippocampus encodes MOS decisions across all types of threat, a vmPFC anterior-posterior gradient tracked threat predictability. The posterior vmPFC encoded for more unpredictable threat and showed functional coupling with the amygdala and hippocampus. Conversely, the anterior vmPFC was more active for the more predictable attacks and showed coupling with the striatum. Our findings suggest that when pre-empting danger, the anterior vmPFC may provide a safety signal, possibly via predictable positive outcomes, while the posterior vmPFC drives prospective danger signals.


2019 ◽  
Author(s):  
Vanessa C. Morita ◽  
João R. Sato ◽  
Marcelo S. Caetano ◽  
André M. Cravo

AbstractInterval timing is fundamental for humans and non-human animals to interact with their environment. Several studies that investigate temporal processing combine behavioural tasks with neurophysiological methods, such as electrophysiological recordings (EEG). However, in the majority of these studies, it is hard to dissociate whether EEG activity reflects temporal or decisional information. In the present study, we investigated how time and decision is encoded in the EEG signal while human participants performed a temporal categorisation task with two different temporal references. Using a combination of evoked potentials and multivariate pattern analysis, we show that: (1) During the interval to-be-timed, both temporal and decisional information are encoded; (2) Activity evoked by the end of the interval encodes almost exclusively decisional information. These results suggest that decisional aspects of the task better explain EEG activity commonly related to temporal processing. The interplay between the encoding of time and decision is consistent with recent proposals that approximate temporal processing with decisional models.


Sign in / Sign up

Export Citation Format

Share Document