Article

See It with Feeling: Affective Predictions during Object Perception

Boston CollegeChestnut Hill, MA 02467, USA.
Philosophical Transactions of The Royal Society B Biological Sciences (Impact Factor: 7.06). 06/2009; 364(1521):1325-34. DOI: 10.1098/rstb.2008.0312
Source: PubMed

ABSTRACT

People see with feeling. We 'gaze', 'behold', 'stare', 'gape' and 'glare'. In this paper, we develop the hypothesis that the brain's ability to see in the present incorporates a representation of the affective impact of those visual sensations in the past. This representation makes up part of the brain's prediction of what the visual sensations stand for in the present, including how to act on them in the near future. The affective prediction hypothesis implies that responses signalling an object's salience, relevance or value do not occur as a separate step after the object is identified. Instead, affective responses support vision from the very moment that visual stimulation begins.

Download full-text

Full-text

Available from: Lisa Feldman Barrett, Apr 19, 2014
  • Source
    • "Thus, the odor context may pre-activate these brain structures and subsequently influence their responses when a face appears in the visual field, possibly boosting the processing of faces. Remember that the extraction of the affective content of visual objects occurs in this time-range (Barrett and Bar, 2009 ). Note that the separable topographies discriminating both odors likely indicate recruitment of distinct neural substrates, possibly because of the difference in affective valence between them and/or intrinsic differences in the nature of the odorants, as the chemical structure of an odorant often determine its affective value (e.g., Khan et al., 2007 ). "

    Full-text · Dataset · Feb 2016
  • Source
    • "First the afferent connectivity of OFC differs from that of the surrounding prefrontal areas, as it receives multimodal sensory input, and afferentation from the anterior cingulate cortex, the dorsolateral prefrontal cortex, the hippocampus, the amygdala and the VS (Wilson et al., 2014) (structures also known to be part of the default network). Accordingly the OFC is a heterogenous associative area that integrates external and internal information in order to embed multimodal representations in a spatio-temporal context reflecting the monetary and affective value of stimuli (Barrett & Bar, 2009). Its efferents are highly intertwined with structures of the reward systems as its glutaminergic neural outflow targets the VS, the VTA, and the PPTgN (Cho et al., 2015;Kable & Glimcher, 2007;Okada et al., 2009), structures canonical for model-free learning (e.g. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Reinforcement learning (RL) is a powerful concept underlying forms of associative learning governed by the use of a scalar reward signal, with learning taking place if expectations are violated. RL may be assessed using model-based and model-free approaches. Model-based reinforcement learning involves the amygdala, the hippocampus, and the orbitofrontal cortex (OFC). The model-free system involves the pedunculopontine-tegmental nucleus (PPTgN), the ventral tegmental area (VTA) and the ventral striatum (VS). Based on the functional connectivity of VS, model-free and model based RL systems center on the VS that by integrating model-free signals (received as reward prediction error) and model-based reward related input computes value. Using the concept of reinforcement learning agent we propose that the VS serves as the value function component of the RL agent. Regarding the model utilized for model-based computations we turned to the proactive brain concept, which offers an ubiquitous function for the default network based on its great functional overlap with contextual associative areas. Hence, by means of the default network the brain continuously organizes its environment into context frames enabling the formulation of analogy-based association that are turned into predictions of what to expect. The OFC integrates reward-related information into context frames upon computing reward expectation by compiling stimulus-reward and context-reward information offered by the amygdala and hippocampus, respectively. Furthermore we suggest that the integration of model-based expectations regarding reward into the value signal is further supported by the efferent of the OFC that reach structures canonical for model-free learning (e.g., the PPTgN, VTA, and VS).
    Full-text · Article · Feb 2016 · Behavioral Neuroscience
    • "In this embodied predictive coding perspective (Pezzulo, 2013), the most plausible causes of events are inferred based on both exteroceptive (what I see) and interoceptive (how do I feel) cues. An affectively charged event, such as the presence of a predator can be recognized and categorized from both its perceptual characteristics and the fear it instills in us – with a form of perception that is not "pure" but affectively biased (Barrett and Bar, 2009). This in turn induces a circular causality; where fear is both a cause and a consequence of (predator) perception. "
    [Show abstract] [Hide abstract]
    ABSTRACT: All organisms must integrate cognition, emotion, and motivation to guide action toward valuable (goal) states, as described by active inference. Within this framework, cognition, emotion, and motivation interact through the (Bayesian) fusion of exteroceptive, proprioceptive, and interoceptive signals, the precision-weighting of prediction errors, and the “affective tuning” of neuronal representations. Crucially, misregulation of these processes may have profound psychopathological consequences.
    No preview · Article · Jul 2015 · Behavioral and Brain Sciences
Show more