Biomedical subjects

Sverker Sikström

Publications and source records attributed to Sverker Sikström.

8 recordsLinked to original sources

PubMed2006

How something can be said about telling more than we can know: on choice blindness and introspection.

The legacy of Nisbett and Wilson's classic article, Telling More Than We Can Know: Verbal Reports on Mental Processes (1977), is mixed. It is perhaps the most cited article in the recent history of consciousness studies, yet no empirical research program currently exists that continues the work presented in the article. To remedy this, we have introduced an experimental paradigm we call choice blindness [Johansson, P., Hall, L., Sikström, S., & Olsson, A. (2005). Failure to detect mismatches between intention and outcome in a simple decision task. Science, 310(5745), 116-119.]. In the choice blindness paradigm participants fail to notice mismatches between their intended choice and the outcome they are presented with, while nevertheless offering introspectively derived reasons for why they chose the way they did. In this article, we use word-frequency and latent semantic analysis (LSA) to investigate a corpus of introspective reports collected within the choice blindness paradigm. We contrast the introspective reasons given in non-manipulated vs. manipulated trials, but find very few differences between these two groups of reports.

Affect↗

PubMed2005

Failure to detect mismatches between intention and outcome in a simple decision task.

A fundamental assumption of theories of decision-making is that we detect mismatches between intention and outcome, adjust our behavior in the face of error, and adapt to changing circumstances. Is this always the case? We investigated the relation between intention, choice, and introspection. Participants made choices between presented face pairs on the basis of attractiveness, while we covertly manipulated the relationship between choice and outcome that they experienced. Participants failed to notice conspicuous mismatches between their intended choice and the outcome they were presented with, while nevertheless offering introspectively derived reasons for why they chose the way they did. We call this effect choice blindness.

Choice Behavior↗

PubMed2005

A model for stochastic drift in memory strength to account for judgments of learning.

Previous research has shown that judgments of learning (JOLs) made immediately after encoding have a low correlation with actual cued-recall performance, whereas the correlation is high for delayed judgments. In this article, the authors propose a formal theory describing the stochastic drift of memory strength over the retention interval to account for the delayed-JOL effect. This is done by first decomposing the aggregated memory strength into exponential functions with slow and fast memory traces. The mean aggregated memory strength shows power-function forgetting curves. The drift of the memory strength is large for immediate JOLs (causing a low predictability) and weak for delayed JOLs (causing a high predictability). Consistent with empirical data, the model makes a novel prediction of JOL asymmetry, or that immediate weak JOLs are more predictive of future performance than are immediate strong JOLs. The JOL distributions for immediate and delayed JOLs are also accounted for.

Cues↗

PubMed2004

The variance reaction time model.

The variance reaction time model (VRTM) is proposed to account for various recognition data on reaction time, the mirror effect, receiver-operating-characteristic (ROC) curves, etc. The model is based on simple and plausible assumptions within a neural network: VRTM is a two layer neural network where one layer represents items and one layer represents contexts. The recognition decision is based on a random walk of nodes activated at recognition. VRTM suggests theoretical constraints on the distributions of nodes activated at recognition and the noise in the random walk. The variability in the net inputs to nodes depends on the item frequency (the number of times that the item has been encoded) and the list length. The essential mechanism that accounts for the empirical data is a non-linear activation function. The mean activation threshold in the non-linear activation function is placed to achieve efficient discriminability between new and old items and there is variability in the activation threshold. VRTM predicts the mirror effect for low and high frequency words, a strength based mirror effect between conditions but not within one condition, appropriate ROC-curves for old/new and high/low frequency items, and list-length effects. Furthermore, it predicts appropriate means and distributions of reaction times for old/new, correct/incorrect, and high/low frequency items as well as speed/accuracy tradeoffs. VRTM has an explicit mathematical solution, it is simulated in a neural network, and it is fitted to a number of datasets.

Attention↗

PubMed2002

Forgetting curves: implications for connectionist models.

Forgetting in long-term memory, as measured in a recall or a recognition test, is faster for items encoded more recently than for items encoded earlier. Data on forgetting curves fit a power function well. In contrast, many connectionist models predict either exponential decay or completely flat forgetting curves. This paper suggests a connectionist model to account for power-function forgetting curves by using bounded weights and by generating the learning rates from a monotonically decreasing function. The bounded weights introduce exponential forgetting in each weight and a power-function forgetting results when weights with different learning rates are averaged. It is argued that these assumptions are biologically reasonable. Therefore power-function forgetting curves are a property that may be expected from biological networks. The model has an analytic solution, which is a good approximation of a power function displaced one lag in time. This function fits better than any of the 105 suggested two-parameter forgetting-curve functions when tested on the most precise recognition memory data set collected by. Unlike the power-function normally used, the suggested function is defined at lag zero. Several functions for generating learning rates with a finite integral yield power-function forgetting curves; however, the type of function influences the rate of forgetting. It is shown that power-function forgetting curves cannot be accounted for by variability in performance between subjects because it requires a distribution of performance that is not found in empirical data. An extension of the model accounts for intersecting forgetting curves found in massed and spaced repetitions. The model can also be extended to account for a faster forgetting rate in item recognition (IR) compared to associative recognition in short but not long retention intervals.

Humans↗

PubMed2002

Integrative neurocomputational perspectives on cognitive aging, neuromodulation, and representation.

Besides neuroanatomical changes, neuromodulatory mechanisms are also compromised during aging. Neural network models are suitable tools for exploring the relatively broad and homogenous neuromodulatory influences on cortical function. Computational approaches for understanding neuromodulation of the dynamic properties of cortical function and recent neurocomputational theories relating different aspects of cognitive aging with declines in neuromodulation are reviewed. Considered within an integrative cross-level neurocomputational framework, aging-related decline in dopaminergic neuromodulation reduces the fidelity of neural information and gives rise to less distinctive neural pattern representations that may underlie various facets of aging cognitive and, possibly also, sensorimotor phenomena.

Aging↗

PubMed2002

The power integration diffusion model for production breaks.

The learning curve during repetitive production and the associated forgetting during production breaks are fundamental issues in the understanding of behavior. A model is suggested that combines 3 basic findings, namely, that single memory traces decay according to a power function of the retention interval, that aggregated memory traces can be combined by integration, and that the time to produce a unit can be described by a diffusion process on the memory trace. This power integration diffusion model is validated with empirical data, and the result fits better than 14 other published forgetting models.

Attention↗

PubMed2001

Aging cognition: from neuromodulation to representation.

Basic cognitive functions, such as the abilities to activate, represent, maintain, focus and process information, decline with age. A paradigm shift towards cross-level conceptions is needed in order to obtain an integrative understanding of cognitive aging phenomena that cuts across neural, information-processing, and behavioral levels. We review empirical data at these different levels, and computational theories proposed to enable their integration. A theoretical link is highlighted, relating deficient neuromodulation with noisy information processing, which might result in less distinctive cortical representations. These less distinctive representations might be implicated in working memory and attentional functions that underlie the behavioral manifestations of cognitive aging deficits.

Journal Article↗