engleski

A Neurodynamical Account of How Emotions Affect Brightness Perception

Recent studies revealed that recalling emotional state or perceiving facial expression alter perception of brightness (Banerjee et al., 2012 ; Meier et al., 2007 ; Song et al., 2012). Here, we developed a neural network architecture based on the adaptive resonance theory (ART) to explain observed findings. In the ART cortical circuit, categorization of sensory input is achieved by matching bottom-up sensory signals with top-down expectations. When the bottom-up and top-down signals are closely aligned, resonant state develops that indicate successful recognition of the input pattern. On the other hand, mismatch between bottom-up and top-down signals produces a global reset wave that clears the traces of erroneous categorization and top-down expectation. According to Grossberg (1999), conscious perception arises from the network resonance. Therefore, recalling the emotional state associated with certain brightness will not produce resonant state if bottom-up signals are not consistent with the top-down prediction based on this recall. However, traces of erroneous prediction can still influence a decision or response preparation stage. We performed computer simulations with the proposed model to show how bias induced by recalled emotional state develops over time and how it shifts the decision to choose the certain brightness level without altering actual brightness perception. In conclusion, we employed established computational mechanisms of the adaptive resonance theory to show that emotions can alter brightness perception only indirectly by influencing decision or response, but not perception itself, which is consistent with the recent proposal of Firestone and Scholl (2016).

perception of brightness, emotions, neural networks, adaptive resonance theory

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