Computational brain & behavior最新文献

Major Depressive Disorder (MDD) is associated with emotional memory deficits, but treatment is limited by a poor understanding of the mechanisms that drive such behavior. Our previous work linked depression to a negative retrieval bias rooted in abnormal evidence accumulation (Cataldo et al., 2023). The Drift Diffusion Model can account for this bias in two ways: increased familiarity, in which depression strengthens evidence for all negative memories-even false ones; or motivated retrieval, in which depression increases the propensity to judge negative items as "old"-even if they are weak. Thus, it is unclear whether depression affects the quality of negative memories or the way they are acted upon. The current work distinguishes these accounts via the Parceling Recognition Into Strength and Motivation (PRISM) task, which isolates memory from decision processes by extending single-item recognition to forced choices between targets and lures (Starns et al., 2018). Though motivation to respond "old" can bias single-item judgments, it should play little or no role when judging which item is old; thus, familiarity is implicated when valence effects extend across both tasks, and motivation is implicated when they do not. In a sample of 53 adults ranging in depressive severity, we found that the negative retrieval bias extended across single-item and forced-choice recognition, thus supporting false familiarity. A qualitative analysis of participants' self-reported strategies further indicated that increased schema use may be an important mechanism. In sum, we provide critical evidence that the negative retrieval bias in depressed adults results from disrupted memory representations.

The Iowa gambling task (IGT) is widely used to study risky decision-making and learning from rewards and punishments. Although numerous cognitive models have been developed using reinforcement learning frameworks to investigate the processes underlying the IGT, no single model has consistently been identified as superior, largely due to the overlooked importance of model flexibility in capturing choice patterns. This study examines whether human reinforcement learning models adequately capture key experimental choice patterns observed in IGT data. Using simulation and parameter space partitioning (PSP) methods, we explored the parameter space of two recently introduced models-Outcome-Representation Learning and Value plus Sequential Exploration-alongside four traditional models. PSP, a global analysis method, investigates what patterns are relevant to the parameters' spaces of a model, thereby providing insights into model flexibility. The PSP study revealed varying potentials among candidate models to generate relevant choice patterns in IGT, suggesting that model selection may be dependent on the specific choice patterns present in a given dataset. We investigated central choice patterns and fitted all models by analyzing a comprehensive data pool (N = 1428) comprising 45 behavioral datasets from both healthy and clinical populations. Applying Akaike and Bayesian information criteria, we found that the Value plus Sequential Exploration model outperformed others due to its balanced potential to generate all experimentally observed choice patterns. These findings suggested that the search for a suitable IGT model may have reached its conclusion, emphasizing the importance of aligning a model's parameter space with experimentally observed choice patterns for achieving high accuracy in cognitive modeling.

Repeated assessments of cognitive performance yield rich data from which we can extract markers of cognitive performance. Computational cognitive process models are often fit to repeated cognitive assessments to quantify individual differences in terms of substantively meaningful cognitive markers and link them to other person-level variables. Most studies stop at this point and do not test whether these cognitive markers have utility for predicting some meaningful outcomes. Here, we demonstrate a partially observable predictor modeling approach that can fill this gap. Using this approach, we can simultaneously extract cognitive markers from repeated assessment data and use these together with demographic covariates for predictive modeling of a clinically interesting outcome in a Bayesian multilevel modeling framework. We describe this approach by constructing a predictive process model in which features of learning are combined with demographic variables to predict mild cognitive impairment and demonstrate it using data from the Einstein Aging Study.

In the computational and experimental psycholinguistic literature, the mechanisms behind syntactic structure building (e.g., combining words into phrases and sentences) are the subject of considerable debate. Much experimental work has shown that surprisal is a good predictor of human behavioral and neural data. These findings have led some authors to model language comprehension in a purely probabilistic way. In this paper, we use simulation to exemplify why surprisal works so well to model human data and to illustrate why exclusive reliance on it can be problematic for the development of mechanistic theories of language comprehension, particularly those with emphasis on meaning composition. Rather than arguing for the importance of structural or probabilistic information to the exclusion or exhaustion of the other, we argue more emphasis should be placed on understanding how the brain leverages both types of information (viz., statistical and structured). We propose that probabilistic information is an important cue to the structure in the message, but is not a substitute for the structure itself-neither computationally, formally, nor conceptually. Surprisal and other probabilistic metrics must play a key role as theoretical objects in any explanatory mechanistic theory of language processing, but that role remains in the service of the brain's goal of constructing structured meaning from sensory input.

Supplementary information: The online version contains supplementary material available at 10.1007/s42113-025-00237-9.

In many scientific fields, sparseness and indirectness of empirical evidence pose fundamental challenges to theory development. Theories of the evolution of human cognition provide a guiding example, where the targets of study are evolutionary processes that occurred in the ancestors of present-day humans. In many cases, the evidence is both very sparse and very indirect (e.g., archaeological findings regarding anatomical changes that might be related to the evolution of language capabilities); in other cases, the evidence is less sparse but still very indirect (e.g., data on cultural transmission in groups of contemporary humans and non-human primates). From examples of theoretical and empirical work in this domain, we distill five virtuous practices that scientists could aim to satisfy when evidence is sparse or indirect: (i) making assumptions explicit, (ii) making alternative theories explicit, (iii) pursuing computational and formal modelling, (iv) seeking external consistency with theories of related phenomena, and (v) triangulating across different forms and sources of evidence. Thus, rather than inhibiting theory development, sparseness or indirectness of evidence can catalyze it. To the extent that there are continua of sparseness and indirectness that vary across domains and that the principles identified here always apply to some degree, the solutions and advantages proposed here may generalise to other scientific domains.