TRoPICALS: a computational embodied neuroscience model of compatibility effects.

By Daniele Caligiore, A. Borghi, Domenico Parisi, Gianluca Baldassarre. 2010

128 citations recorded by the source corpus.

Perceiving objects activates the representation of their affordances. For example, experiments on compatibility effects showed that categorizing objects by producing certain handgrips (power or precision) is faster if the requested responses are compatible with the affordance elicited by the size of objects (e.g., small or large). The article presents a neural-network architecture that provides a general framework to account for compatibility effects. The model was designed with a methodological approach (computational embodied neuroscience) that aims to provide increasingly general accounts of brain and behavior (4 sources of constraints are used: neuroscientific data, behavioral data, embodied systems, reproduction of learning processes). The model is based on 4 principles of brain organization that we claim underlie most compatibility effects. First, visual perception and action are organized in the brain along a dorsal neural pathway encoding affordances and a ventral pathway encoding goals. Second, the prefrontal cortex within the ventral pathway gives a top-down bias to action selection by integrating information on stimuli, context, and goals. Third, reaction times depend on dynamic neural competitions for action selection that integrate bottom-up and top-down information. The congruence or incongruence between affordances and goals explains the different reaction times found in the experiments. Fourth, as words trigger internal simulations of their referents, they can cause compatibility effects as objects do. We validated the model by reproducing and explaining 3 types of compatibility effects and showed its heuristic power by producing 2 testable predictions. We also assessed the explicative power of the model by comparing it with related models and showed how it can be extended to account for other compatibility effects.

Subfields: Cognitive Neuroscience, Social Psychology, Neuroscience, Cognitive Science & Psychology

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Citations in the corpus

What can the brain teach us about building artificial intelligence? (2016, background): “Even higher-level “ventral” pathways inform the brain on the identity and nature of resources in the environment to support decisions on what to do (Milner & Goodale, 2006; Caligiore et al., 2010).”
Neural and Computational Mechanisms of Action Processing: Interaction between Visual and Motor Representations. (2015, methodology): “A similar theoretical approach is also followed by models of other groups, such as the TROPICAL model (Caligiore et al., 2010), which exploits a variety of classical neural network techniques (including also Kohonen maps, neural ﬁelds, and hierarchical object recognition architectures) in order to…”
The Hierarchical Organisation of Cortical and Basal-Ganglia Systems: A Computationally-Informed Review and Integrated Hypothesis (2013, unknown): “This section will present a more detailed view of the organisation of the cortical hierarchy and its functioning and learning mechanisms by reviewing in depth a specific computational model by the authors (Caligiore et al., 2010, 2012).”
Consensus Paper: Towards a Systems-Level View of Cerebellar Function: the Interplay Between Cerebellum, Basal Ganglia, and Cortex (2016, background): “Supporting this idea is the fact that the neuronal systems for mental imagery and motor preparation are closely related [20, 146–148, 153, 154].”