Tomasz Smolen

An oscillatory model of individual differences in working memory capacity and relational integration

We present a novel computational model of the active buffer of working memory (WM). The model uses synchronous oscillations in order to bind an item and its corresponding context into one representation, while asynchronous oscillations are used to separate the representations. Due to the bindings, the model can ascribe proper meanings to items, as demonstrated by the replication of the effective rejection of distractors. The model predicts the inherent limitation of WM capacity in range of 1 to around six items that arises from the trade-off between the number and stability of separate oscillations. This trade-off depends on the strength of lateral inhibition exerted. The systematic variation in inhibition led to the exact replication of capacity distribution observed in a large sample, as well as to the prediction of a few novel capacity-related experimental effects. Finally, we showed that the differences in capacity can underlie the differences in a more complex ability of detecting relations governing a pattern of stimuli, called relational integration, which is known to be strongly related to the effectiveness of higher-order cognitive processing.

An Integrated Utility-Based Model of Conflict Evaluation and Resolution in the Stroop Task

Cognitive control allows humans to direct and coordinate their thoughts and actions in a flexible way, in order to reach internal goals regardless of interference and distraction. The hallmark test used to examine cognitive control is the Stroop task, which elicits both the weakly learned but goal-relevant and the strongly learned but goal-irrelevant response tendencies, and requires people to follow the former while ignoring the latter. After reviewing the existing computational models of cognitive control in the Stroop task, its novel, integrated utility-based model is proposed. The model uses 3 crucial control mechanisms: response utility reinforcement learning, utility-based conflict evaluation using the Festinger formula for assessing the conflict level, and top-down adaptation of response utility in service of conflict resolution. Their complex, dynamic interaction led to replication of 18 experimental effects, being the largest data set explained to date by 1 Stroop model. The simulations cover the basic congruency effects (including the response latency distributions), performance dynamics and adaptation (including EEG indices of conflict), as well as the effects resulting from manipulations applied to stimulation and responding, which are yielded by the extant Stroop literature.

The quadratic relationship between difficulty of intelligence test items and their correlations with working memory

Fluid intelligence (Gf) is a crucial cognitive ability that involves abstract reasoning in order to solve novel problems. Recent research demonstrated that Gf strongly depends on the individual effectiveness of working memory (WM). We investigated a popular claim that if the storage capacity underlay the WM–Gf correlation, then such a correlation should increase with an increasing number of items or rules (load) in a Gf-test. As often no such link is observed, on that basis the storage-capacity account is rejected, and alternative accounts of Gf (e.g., related to executive control or processing speed) are proposed. Using both analytical inference and numerical simulations, we demonstrated that the load-dependent change in correlation is primarily a function of the amount of floor/ceiling effect for particular items. Thus, the item-wise WM correlation of a Gf-test depends on its overall difficulty, and the difficulty distribution across its items. When the early test items yield huge ceiling, but the late items do not approach floor, that correlation will increase throughout the test. If the early items locate themselves between ceiling and floor, but the late items approach floor, the respective correlation will decrease. For a hallmark Gf-test, the Raven-test, whose items span from ceiling to floor, the quadratic relationship is expected, and it was shown empirically using a large sample and two types of WMC tasks. In consequence, no changes in correlation due to varying WM/Gf load, or lack of them, can yield an argument for or against any theory of WM/Gf. Moreover, as the mathematical properties of the correlation formula make it relatively immune to ceiling/floor effects for overall moderate correlations, only minor changes (if any) in the WM–Gf correlation should be expected for many psychological tests.

Neural Underpinnings of Decision Strategy Selection: A Review and a Theoretical Model

In multi-attribute choice, decision makers use decision strategies to arrive at the final choice. What are the neural mechanisms underlying decision strategy selection? The first goal of this paper is to provide a literature review on the neural underpinnings and cognitive models of decision strategy selection and thus set the stage for a neurocognitive model of this process. The second goal is to outline such a unifying, mechanistic model that can explain the impact of noncognitive factors (e.g., affect, stress) on strategy selection. To this end, we review the evidence for the factors influencing strategy selection, the neural basis of strategy use and the cognitive models of this process. We also present the Bottom-Up Model of Strategy Selection (BUMSS). The model assumes that the use of the rational Weighted Additive strategy and the boundedly rational heuristic Take The Best can be explained by one unifying, neurophysiologically plausible mechanism, based on the interaction of the frontoparietal network, orbitofrontal cortex, anterior cingulate cortex and the brainstem nucleus locus coeruleus. According to BUMSS, there are three processes that form the bottom-up mechanism of decision strategy selection and lead to the final choice: (1) cue weight computation, (2) gain modulation, and (3) weighted additive evaluation of alternatives. We discuss how these processes might be implemented in the brain, and how this knowledge allows us to formulate novel predictions linking strategy use and neural signals.