Cristian Buc Calderon

Setting the stage subliminally: Unconscious context effects

An important approach to understand how the brain gives rise to consciousness is to probe the depth of unconscious processing, thus to define the key features that cause conscious awareness. Here, we investigate the possibility for subliminal stimuli to shape the context for unconscious processing. Context effects have generally been assumed to require consciousness. In the present experiment, unconscious context processing was investigated by looking at the impact of the context on the response activation elicited by a subliminal prime. We compared the effects of the same subliminal prime on target processing when the prime was embedded in different unconscious contexts. Results showed that the same prime can evoke opposite responses depending on the unconscious context in which it is presented. Taken together, the results of this study show that context effects can be unconscious.

Losing the Boundary: Cognition Biases Action Well After Action Selection

For selecting an action, traditional theories suggest a cognitive architecture made of serial processing units. Others suggested that action selection emerges from the parallel implementation of and competition between multiple action plans. To disentangle these 2 hypotheses, we created a reaching task assessing the temporal dynamics of action selection. Crucially, our design did not force action selection processes to operate in parallel, allowing an informative comparison between the hypotheses. We manipulated the probability of congruence between a cue and a delayed reach target to investigate, in an unbiased way, whether congruence probability interacts with reach trajectory. Our results show that reach trajectories are modulated by the probability of congruence. Hence, action selection is temporally spread, continues after movement onset, and emerges from a competition between multiple afforded action plans, in parallel biased by relevant task factors (e.g., probability of reach).

Avoiding the Conflict: Metacognitive Awareness Drives the Selection of Low-Demand Contexts

Previous research attempted to explain how humans strategically adapt behavior in order to achieve successful task performance. Recently, it has been suggested that 1 potential strategy is to avoid tasks that are too demanding. Here, we report 3 experiments that investigate the empirically neglected role of metacognitive awareness in this process. In these experiments, participants could freely choose between performing a task in either a high-demand or a low-demand context. Using subliminal priming, we ensured that participants were not aware of the visual stimuli creating these different demand contexts. Our results showed that participants who noticed a difference in task difficulty (i.e., metacognitive aware participants) developed a clear preference for the low-demand context. In contrast, participants who experienced no difference in task difficulty (i.e., metacognitive unaware participants) based their choices on variables unrelated to cognitive demand (e.g., the color or location associated with a context), and did not develop a preference for the low-demand context. Crucially, this pattern was found despite identical task performance in both metacognitive awareness groups. A multiple regression approach confirmed that metacognitive awareness was the main factor driving the preference for low-demand contexts. These results argue for an important role of metacognitive awareness in the strategic avoidance of demanding tasks.

The unfolding action model of initiation times, movement times, and movement paths.

Converging evidence has led to a consensus in favor of computational models of behavior implementing continuous information flow and parallel processing between cognitive processing stages. Yet, such models still typically implement a discrete step between the last cognitive stage and motor implementation. This discrete step is implemented as a fixed decision bound that activation in the last cognitive stage needs to cross before action can be initiated. Such an implementation is questionable as it cannot account for two important features of behavior. First, it does not allow to select an action while withholding it until the moment is appropriate for executing it. Second, it cannot account for recent evidence that cognition is not confined prior to movement initiation, but consistently leaks into movement. To address these two features, we propose a novel neurocomputational model of cognition-action interactions, namely the unfolding action model (UAM). Crucially, the model implements adaptive information flow between the last cognitive processing stage and motor implementation. We show that the UAM addresses the two abovementioned features. Empirically, the UAM accounts for traditional response time data, including positively skewed initiation time distribution, functionally fixed decision bounds and speed–accuracy trade-offs in button-press experimental designs. Moreover, it accounts for movement times, movement paths, and how they are influenced by cognitive-experimental manipulations. This move should close the current gap between abstract decision-making models and behavior observed in natural habitats.

Continuous track paths reveal additive evidence integration in multistep decision making

Significance Many daily-life decisions consist of multiple steps (e.g., go outside, go left, arrive at Italian restaurant). We distinguish four prominent models of such multistep decision making. We further propose a paradigm in two experiments to disentangle these models. Only the models implementing additive integration from second- to first-step choices were able to account for track path movements. Specifically, we find that first-step decisions are initially based on the sum/mean of second-step future rewards. As information regarding the optimal second-step choice increases, the decision gradually becomes based on the maximal future reward. Hence, we suggest that multistep decision making involves progressive unraveling of future outcomes during decision making. Multistep decision making pervades daily life, but its underlying mechanisms remain obscure. We distinguish four prominent models of multistep decision making, namely serial stage, hierarchical evidence integration, hierarchical leaky competing accumulation (HLCA), and probabilistic evidence integration (PEI). To empirically disentangle these models, we design a two-step reward-based decision paradigm and implement it in a reaching task experiment. In a first step, participants choose between two potential upcoming choices, each associated with two rewards. In a second step, participants choose between the two rewards selected in the first step. Strikingly, as predicted by the HLCA and PEI models, the first-step decision dynamics were initially biased toward the choice representing the highest sum/mean before being redirected toward the choice representing the maximal reward (i.e., initial dip). Only HLCA and PEI predicted this initial dip, suggesting that first-step decision dynamics depend on additive integration of competing second-step choices. Our data suggest that potential future outcomes are progressively unraveled during multistep decision making.