2017年6月28日水曜日

Approach-Induced Biases in Human Information

Hunt LT, Rutledge RB, Malalasekera WM, Kennerley SW, Dolan RJ
PLoS Biol. 2016 Nov 10;14(11):e2000638. doi: 10.1371/journal.pbio.2000638.

Information sampling is often biased towards seeking evidence that confirms one's prior beliefs. Despite such biases being a pervasive feature of human behavior, their underlying causes remain unclear. Many accounts of these biases appeal to limitations of human hypothesis testing and cognition, de facto evoking notions of bounded rationality, but neglect more basic aspects of behavioral control. Here, we investigated a potential role for Pavlovian approach in biasing which information humans will choose to sample. We collected a large novel dataset from 32,445 human subjects, making over 3 million decisions, who played a gambling task designed to measure the latent causes and extent of information-sampling biases. We identified three novel approach-related biases, formalized by comparing subject behavior to a dynamic programming model of optimal information gathering. These biases reflected the amount of information sampled ("positive evidence approach"), the selection of which information to sample ("sampling the favorite"), and the interaction between information sampling and subsequent choices ("rejecting unsampled options"). The prevalence of all three biases was related to a Pavlovian approach-avoid parameter quantified within an entirely independent economic decision task. Our large dataset also revealed that individual differences in the amount of information gathered are a stable trait across multiple gameplays and can be related to demographic measures, including age and educational attainment. As well as revealing limitations in cognitive processing, our findings suggest information sampling biases reflect the expression of primitive, yet potentially ecologically adaptive, behavioral repertoires. One such behavior is sampling from options that will eventually be chosen, even when other sources of information are more pertinent for guiding future action.

2017年6月26日月曜日

A basal ganglia circuit for evaluating action outcomes

Stephenson-Jones M, Yu K, Ahrens S, Tucciarone JM, van Huijstee AN, Mejia LA, Penzo MA, Tai LH, Wilbrecht L, Li B.
Nature. 2016 Nov 10;539(7628):289-293. doi: 10.1038/nature19845. Epub 2016 Sep 21.

The basal ganglia, a group of subcortical nuclei, play a crucial role in decision-making by selecting actions and evaluating their outcomes. While much is known about the function of the basal ganglia circuitry in selection, how these nuclei contribute to outcome evaluation is less clear. Here we show that neurons in the habenula-projecting globus pallidus (GPh) in mice are essential for evaluating action outcomes and are regulated by a specific set of inputs from the basal ganglia. We find in a classical conditioning task that individual mouse GPh neurons bidirectionally encode whether an outcome is better or worse than expected. Mimicking these evaluation signals with optogenetic inhibition or excitation is sufficient to reinforce or discourage actions in a decision-making task. Moreover, cell-type-specific synaptic manipulations reveal that the inhibitory and excitatory inputs to the GPh are necessary for mice to appropriately evaluate positive and negative feedback, respectively. Finally, using rabies-virus-assisted monosynaptic tracing, we show that the GPh is embedded in a basal ganglia circuit wherein it receives inhibitory input from both striosomal and matrix compartments of the striatum, and excitatory input from the 'limbic' regions of the subthalamic nucleus. Our results provide evidence that information about the selection and evaluation of actions is channelled through distinct sets of basal ganglia circuits, with the GPh representing a key locus in which information of opposing valence is integrated to determine whether action outcomes are better or worse than expected.

2017年6月22日木曜日

Neural ensemble dynamics underlying a long-term associative memory

Grewe BF, Gründemann J, Kitch LJ, Lecoq JA, Parker JG, Marshall JD, Larkin MC, Jercog PE, Grenier F, Li JZ, Lüthi A, Schnitzer MJ
Nature. 2017 Mar 30;543(7647):670-675. doi: 10.1038/nature21682. Epub 2017 Mar 22.

The brain's ability to associate different stimuli is vital for long-term memory, but how neural ensembles encode associative memories is unknown. Here we studied how cell ensembles in the basal and lateral amygdala encode associations between conditioned and unconditioned stimuli (CS and US, respectively). Using a miniature fluorescence microscope, we tracked the Ca2+ dynamics of ensembles of amygdalar neurons during fear learning and extinction over 6 days in behaving mice. Fear conditioning induced both up- and down-regulation of individual cells' CS-evoked responses. This bi-directional plasticity mainly occurred after conditioning, and reshaped the neural ensemble representation of the CS to become more similar to the US representation. During extinction training with repetitive CS presentations, the CS representation became more distinctive without reverting to its original form. Throughout the experiments, the strength of the ensemble-encoded CS-US association predicted the level of behavioural conditioning in each mouse. These findings support a supervised learning model in which activation of the US representation guides the transformation of the CS representation.

2017年6月20日火曜日

Nonpolitical Images Evoke Neural Predictors of Political Ideology

Woo-Young Ahn, Kenneth T. Kishida, Xiaosi Gu, Terry Lohrenz, Ann Harvey, John R. Alford, Kevin B. Smith, Gideon Yaffe, John R. Hibbing, Peter Dayan, P. Read Montague
Current Biology. Volume 24, Issue 22, p2693–2699, 17 November 2014

Political ideologies summarize dimensions of life that define how a person organizes their public and private behavior, including their attitudes associated with sex, family, education, and personal autonomy [ 1, 2 ]. Despite the abstract nature of such sensibilities, fundamental features of political ideology have been found to be deeply connected to basic biological mechanisms [ 3–7 ] that may serve to defend against environmental challenges like contamination and physical threat [ 8–12 ]. These results invite the provocative claim that neural responses to nonpolitical stimuli (like contaminated food or physical threats) should be highly predictive of abstract political opinions (like attitudes toward gun control and abortion) [ 13 ]. We applied a machine-learning method to fMRI data to test the hypotheses that brain responses to emotionally evocative images predict individual scores on a standard political ideology assay. Disgusting images, especially those related to animal-reminder disgust (e.g., mutilated body), generate neural responses that are highly predictive of political orientation even though these neural predictors do not agree with participants’ conscious rating of the stimuli. Images from other affective categories do not support such predictions. Remarkably, brain responses to a single disgusting stimulus were sufficient to make accurate predictions about an individual subject’s political ideology. These results provide strong support for the idea that fundamental neural processing differences that emerge under the challenge of emotionally evocative stimuli may serve to structure political beliefs in ways formerly unappreciated.

2017年6月5日月曜日

How prior preferences determine decision-making frames and biases in the human brain

Lopez-Persem A, Domenech P, Pessiglione M
Elife. 2016 Nov 19;5. pii: e20317. doi: 10.7554/eLife.20317.

Understanding how option values are compared when making a choice is a key objective for decision neuroscience. In natural situations, agents may have a priori on their preferences that create default policies and shape the neural comparison process. We asked participants to make choices between items belonging to different categories (e.g., jazz vs. rock music). Behavioral data confirmed that the items taken from the preferred category were chosen more often and more rapidly, which qualified them as default options. FMRI data showed that baseline activity in classical brain valuation regions, such as the ventromedial Prefrontal Cortex (vmPFC), reflected the strength of prior preferences. In addition, evoked activity in the same regions scaled with the default option value, irrespective of the eventual choice. We therefore suggest that in the brain valuation system, choices are framed as comparisons between default and alternative options, which might save some resource but induce a decision bias.