Face Validity of Remembering and Knowing: Empirical Consensus and Disagreement Between Participants and Researchers

2020 ◽  
Vol 15 (6) ◽  
pp. 1400-1422
Author(s):  
Sharda Umanath ◽  
Jennifer H. Coane

Ever since Endel Tulving first distinguished between episodic and semantic memory, the remember/know paradigm has become a standard means of probing the phenomenology of participants’ memorial experiences by memory researchers, neuropsychologists, neuroscientists, and others. However, this paradigm has not been without its problems and has been used to capture many different phenomenological experiences, including retrieval from episodic versus semantic memory, recollection versus familiarity, strength of memory traces, and so on. We first conducted a systematic review of its uses across the literature and then examined how memory experts, other cognitive psychology experts, experts in other areas of psychology, and lay participants (Amazon Mechanical Turk workers) define what it means when one says “I remember” and “I know.” From coding their open-ended responses using a number of theory-bound dimensions, it seems that lay participants do not see eye to eye with memory experts in terms of associating “I remember” responses with recollection and “I know” responses with familiarity. However, there is general consensus with Tulving’s original distinction, linking remembering with memory for events and knowing with semantic memory. Recommendations and implications across fields are discussed.

2021 ◽  
pp. 095679762097577
Author(s):  
Matthew Fisher ◽  
Daniel M. Oppenheimer

Cognitive ability consists not only of one’s internal competence but also of the augmentation offered by the outside world. How much of our cognitive success is due to our own abilities, and how much is due to external support? Can we accurately draw that distinction? Here, we explored when and why people are unaware of their reliance on outside assistance. Across eight experiments ( N = 2,440 participants recruited from Amazon Mechanical Turk), people showed improved metacognitive calibration when assistance occurred after a delay or required active choice. Furthermore, these findings apply across a wide range of cognitive tasks, including semantic memory (Experiments 1a and 1b), episodic memory (Experiments 2a and 2b), and problem solving (Experiments 3a–3d). These experiments offer important insights into how we understand our own abilities when we rely on outside help.


2021 ◽  
Vol 74 ◽  
pp. 101728
Author(s):  
Carolyn M. Ritchey ◽  
Toshikazu Kuroda ◽  
Jillian M. Rung ◽  
Christopher A. Podlesnik

2011 ◽  
Vol 37 (2) ◽  
pp. 413-420 ◽  
Author(s):  
Karën Fort ◽  
Gilles Adda ◽  
K. Bretonnel Cohen

2015 ◽  
Vol 16 (S1) ◽  
Author(s):  
John WG Seamons ◽  
Marconi S Barbosa ◽  
Jonathan D Victor ◽  
Dominique Coy ◽  
Ted Maddess

Author(s):  
F. Jurčíček ◽  
S. Keizer ◽  
Milica Gašić ◽  
François Mairesse ◽  
B. Thomson ◽  
...  

2018 ◽  
Vol 141 (2) ◽  
Author(s):  
Christian E. Lopez ◽  
Scarlett R. Miller ◽  
Conrad S. Tucker

The objective of this work is to explore the possible biases that individuals may have toward the perceived functionality of machine generated designs, compared to human created designs. Toward this end, 1187 participants were recruited via Amazon mechanical Turk (AMT) to analyze the perceived functional characteristics of both human created two-dimensional (2D) sketches and sketches generated by a deep learning generative model. In addition, a computer simulation was used to test the capability of the sketched ideas to perform their intended function and explore the validity of participants' responses. The results reveal that both participants and computer simulation evaluations were in agreement, indicating that sketches generated via the deep generative design model were more likely to perform their intended function, compared to human created sketches used to train the model. The results also reveal that participants were subject to biases while evaluating the sketches, and their age and domain knowledge were positively correlated with their perceived functionality of sketches. The results provide evidence that supports the capabilities of deep learning generative design tools to generate functional ideas and their potential to assist designers in creative tasks such as ideation.


Author(s):  
Kenneth Nemire

This article describes the results of a survey intended as a preliminary assessment of consumer perceptions of the hazardousness of portable ladders and the warning labels provided on portable ladders. One hundred ten participants responded to an online survey tool called Amazon Mechanical Turk. The survey collected information about participants’ use of ladders, their ratings of familiarity with ladders, perceived hazardousness of portable ladders, and perception of warning labels on portable ladders. Results indicated a small but significant relationship between familiarity with ladders and their perceived hazardousness, and that participants thought that people should be warned about the hazards associated with ladder use. Implications for future research about consumer perception of portable ladder hazards and warnings are described.


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