Trends in Cognitive Sciences最新文献

Do goals, beliefs, and desires affect visual experience? This question has long been controversial in cognitive science. There exists extensive literature documenting motivational effects on perceptual reports, but these findings could reflect biases in what people report seeing rather than what they see. Here, we propose that examining the underlying neurocomputational processes can provide new perspectives on this longstanding debate. We review evidence suggesting that motivation biases both perception and action, but does so via distinct neural systems: amygdala and locus coeruleus (LC)-norepinephrine (NE) activity enhances sensory representations for desirable stimuli, while striatal dopamine biases action selection toward goal-congruent actions. The neurocomputational approach provides a framework to advance a mechanistic understanding of motivated seeing and how these biases are shaped by context.

Blindsight patients lack conscious visual perception yet perform visual tasks effectively, suggesting many animals may similarly rely on non-conscious vision. Here, we discuss how to investigate visual consciousness in miniature brains, using bees as a case study. This new endeavor can reveal the minimal neural requirements for visual awareness.

Accurate processing of speech requires that listeners map temporally unfolding input to words. A long-held set of principles describes this process: lexical items are activated immediately and incrementally as speech arrives, perceptual and lexical representations rapidly decay to make room for new information; and lexical entries are temporally structured. In this framework; speech processing is tightly coupled to the temporally unfolding input. However, recent work challenges this: low-level auditory and higher-level lexical representations do not decay and are instead retained over long durations, speech perception may require encapsulated memory buffers, lexical representations are not strictly temporally structured, and listeners can substantially delay lexical access in some circumstances. These findings suggest that current theories and models of word recognition need to be reconceptualized.

Establishing causal relationships between neural activity and brain function requires experimental perturbations of neural activity. Many existing perturbation methods modify activity by directly applying external signals to the brain. We review an alternative approach where brain-computer interfaces (BCIs) leverage volitional control of neural activity to manipulate and causally perturb it. We highlight the potential of BCIs to manipulate neural activity in ways that are flexible, accurate, and adhere to intrinsic biophysical and network-level constraints to investigate the consequences of configuring neural population activity in specified ways. We discuss the advantages and disadvantages of using BCIs as a perturbation tool compared with other perturbation methods and how BCIs can expand the scope of questions that can be addressed about brain function.

Explaining how children build a language system is a central goal of research in language acquisition, with broad implications for language evolution, adult language processing, and artificial intelligence (AI). Here, we propose a constructivist framework for future theory-building in language acquisition. We describe four components of constructivism, drawing on wide-ranging evidence to argue that theories based on these components will be well suited to explaining developmental change. We show how adopting a constructivist framework both provides plausible answers to old questions (e.g., how children build linguistic representations from their input) and generates new questions (e.g., how children adapt to the affordances provided by different cultures and languages).

Studies of perceptual decision-making typically present the same stimulus repeatedly over the course of an experimental session but ignore the order of these observations, assuming unrealistic stability of decision strategies over trials. However, even 'stable,' 'steady-state,' or 'expert' decision-making behavior features significant trial-to-trial variability that is richly structured in time. Structured trial-to-trial variability of various forms can be uncovered using latent variable models such as hidden Markov models and autoregressive models, revealing how unobservable internal states change over time. Capturing such temporal structure can avoid confounds in cognitive models, provide insights into inter- and intraindividual variability, and bridge the gap between neural and cognitive mechanisms of variability in perceptual decision-making.

Climate anxiety has both positive and negative potential. It can spur action or hinder it, while taxing mental health. The key lies in balance: a Goldilocks zone wherein anxiety motivates without overwhelming. Cognitive processes, including threat and coping appraisals and future-oriented thinking, may help sustain this adaptive equilibrium.