Do Cognitive Styles Affect Psychophysical Tasks Performance?

Abstract

Background. In order to deepen understanding of signal detection/discrimination processes we have to focus on highlighting individual differences in observers’ sensory performance due to the contribution of various variables of personality and cognition spheres. The purpose of our study was to test cognitive style factors (augmenting-reducing, levelling-sharpening, flexibility-rigidity of cognitive control, equivalence range, and focusing-scanning) influencing performance of psychophysical tasks. Methods. Ninety participants performed a set of cognitive style tests as well as two psychophysical tasks on visual signal detection (‘yes-no’) and loudness discrimination (‘same-different’). The duration of visual pattern presentation and difference between pairs of auditory stimuli were used to provide task’s difficulty level, and therefore the level of uncertainty. Results. Data analysis showed several effects of cognitive styles on psychophysical tasks performance indices, in particular: sensory sensitivity, RT and its stability, and response confidence. According to our results, each style is related to its own benefits and advantages in observer’s overall productivity. Furthermore, the contribution of cognitive styles differed depending on task’s type and difficulty level. Conclusions. Our results support current findings, considering cognitive styles as playing a regulative role in cognitive activity. Hence, they could be acknowledged as tools, mediating individual strategies, representing different ways of coping with perceptual uncertainty.

Keywords: Cognitive stylespsychophysicsindividual differencessignal detectionsignal discrimination

Introduction

Multiple studies showed the effect of subject or personality factors on observer’s sensory performance, which nonetheless were rarely taken into consideration. However, it was later acknowledged that contribution of stimulation conditions, or ‘stimulus factors’, provides only partial explanation of perceiver’s behaviour, emphasizing the necessity to consider subject factors as well (Gurler et al., 2015; Siegel, Kelly, 2017; Wilson et al., 2016).

We believe that threshold task performance is associated with a conflict between the need to solve the task successfully and observer’s available resources. It has been widely studied in line with resource approach, suggesting that cognitive processes represent the system with limited amount of available cognitive resources. Resources, for their part, are considered as a pool of energy, flexibly distributed during information processing (Epling et al., 2016; Humphreys, Revelle, 1984; Parasuraman et al., 1987; Ralph et al., 2017; Smit et al., 2004; Thomson et al., 2015). Demands and conditions of psychophysical tasks serve as a basis for engaging the methodology of resource approach since they impose several restrictions to observer’s available resources.

The stated conflict may be solved in different ways, reflected in the choice of various tools and strategies, regulated, in its turn, by high-level mechanisms of observer’s mental activity. We suggest cognitive styles (CS) as one of such regulative mechanisms.

The concept of CS was initially introduced as a result of considering a qualitatively different aspect of human cognitive sphere, referred to individual differences in a way or manner of cognitive functioning, i.e. organizing, representing and processing information (Kozhevnikov et al., 2014; Zhang et al., 2012). In accordance with this approach, same results of solving various types of tasks can be achieved in different ways, such as ways of perceiving and understanding the task, as well as various characteristics of tempo, efficiency and mistakes made (Nosal, 2009). Hence, this is an important factor to examine since CS could provide an explanation of individual variabilities in threshold tasks performance.

Problem Statement

There is a large body of research that has been conducted into the role of stimulus factors in observer’s performance indices and strategies used, whereas the contribution and weight of individual differences factors is still underestimated in psychophysics. While the final performance can be partially explained by stimulus variables, the study of individual differences is an issue of critical importance.

We would like to highlight the crucial point that psychophysical tasks have to be considered as related to high workload and high perceptual uncertainty. As a result, such task specifities and demands force observer to look for any appropriate compensation strategies, which allow overcoming the distinct deficit of sensory information.

The present tasks cause resource costs due to (1) random and rapid presentation of low-intensive stimulation, (2) monotonous and long-term procedure, (3) successive character of stimuli presentation, (4) spatial uncertainty in localisation of target stimulus. In addition to mentioned above, these vigilance tasks call for engagement of additional effort, reflected in short-term memory and attention allocation resources. Furthermore, observer has to maintain a high vigilance level and sustain high tempo over prolonged period of performing the task.

Research Questions

In this study we have endeavoured to address two main issues, referring to the wide psychological context of individual differences role in cognitive tasks solving. First, we were wondering whether differences in sensory performance exist between subjects representing certain CS, and hence, whether CS studies are fruitful avenue in differential psychophysics. The second addressed issue was the following: does the contribution of CS factors change due to variation of task type and level of perceptual uncertainty?

Purpose of the Study

The purpose of our study was to test CS factors (augmenting-reducing, levelling-sharpening, flexibility-rigidity of cognitive control, equivalence range, and focusing-scanning) influencing sensory performance indices under different task type (visual signal detection – YN , and auditory signal discrimination – SD tasks) and levels of perceptual uncertainty.

Research Methods

Stimuli

In YN task stimuli were visual patterns consisting of six letters (Times New Roman font, size 16). The horizontal distance between letters was 35 mm, the vertical one was 55 mm. Three stimuli were used: ‘signal’, ‘noise’, and ‘distractor’ (fig. 01). In SD task stimuli were two 1000 Hz tones 200 ms duration with ISI 500 ms. ITI was 2500 ms for both tasks.

The duration of visual pattern presentation (90 or 60 ms) and difference between pairs of auditory stimuli (2 or 1 dB) were used to provide task’s difficulty level, and therefore the level of uncertainty. Each task consisted of five series: (1) introductory – 10 trials, (2) easy training – 30 trials, (3) hard training – 30 trials, (4) main easy – 100 trials, and (5) main hard – 100 trials.

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Findings

One-way ANOVAs showed several significant effects of CS on psychophysical tasks performance indices. The Tables 1 - 5 present only significant (p < 0.05) and quasi-significant (0.05 < p < 0.1) effects.

Conclusion

To conclude, our approach allows considering both subject’s activity (CS) and stimulus factors (task type and uncertainty level), determining observer’s performance.

The results are crucial in the light of the controversial issue of style value (Zhang et al., 2012). We believe that each style is related to its own benefits and advantages — for instance, either accuracy or speed of performing a task. Neither of style groups should be considered as more or less effective in general; they rather may or may not correspond to the certain conditions and demands in solving the particular cognitive task. However, we stress once again that the same final efficiency could be associated with different resource costs, and furthermore could be achieved in different ways or manners.

We would like to highlight the crucial point that CS should be considered as tools, mediating individual strategies representing different ways of coping with perceptual uncertainty. This understanding is supported by up-to-date findings concerning the role of CS in cognition regulation (Kozhevnikov et al., 2014; Zhang et al., 2012).

Acknowledgments

This study was supported in part by the Lomonosov Moscow State University Program of Development. We acknowledge A. Kremlev for the development of computer tests we used.

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