Spontaneous Task Structure Formation Results in a Cost to Incidental Memory of Task Stimuli

Abstract

Humans are characterized by their ability to leverage rules for classifying and linking stimuli to context-appropriate actions. Previous studies have shown that when humans learn stimulus-response associations for two-dimensional stimuli, they implicitly form and generalize hierarchical rule structures (task-sets). However, the cognitive processes underlying structure formation are poorly understood. Across four experiments, we manipulated how trial-unique images mapped onto responses to bias spontaneous task-set formation and investigated structure learning through the lens of incidental stimulus encoding. Participants performed a learning task designed to either promote task-set formation (by "motor-clustering" possible stimulus-action rules), or to discourage it (by using arbitrary category-response mappings). We adjudicated between two hypotheses: Structure learning may promote attention to task stimuli, thus resulting in better subsequent memory. Alternatively, building tasksets might impose cognitive demands (for instance, on working memory) that divert attention away from stimulus encoding. While the clustering manipulation affected task-set formation, there were also substantial individual differences. Importantly, structure learning incurred a cost: spontaneous task-set formation was associated with diminished stimulus encoding. Thus, spontaneous hierarchical task-set formation appears to involve cognitive demands that divert attention away from encoding of task stimuli during structure learning.

Semantic influences on episodic memory distortions.

“…This line of reasoning was inspired by observations that stimuli are often better remembered when they violate or are irrelevant to a salient task structure (Goodman, 1980;Love, Medin, & Gureckis, 2004;Sakamoto & Love, 2004;Bejjani & Egner, 2019).…”

“…Here, we probe whether this difference in bias by typicality is a consequence of participants’ orientation toward category membership during encoding. As highlighted in the Introduction, it is well known that different encoding strategies give rise to different patterns of memory, where tasks that encourage relational encoding result in prioritized memory for distinctive or exception items by highlighting their dissimilarity with other encoded items (Bejjani & Egner, 2019; Goodman, 1980; Hunt & McDaniel, 1993; Love et al, 2004; Sakamoto & Love, 2004). Furthermore, a salient organizational structure can change which features of an item are more deeply encoded.…”

Evaluating the learning of stimulus-control associations through incidental memory of reinforcement events

“…As in previous studies (Bejjani, Tan, et al, 2020;Bejjani & Egner, 2019), we obtained 544 feedback images from an online database (http://cabezalab.org/cabezalabobjects/) and Google searches of images with a license for noncommercial reuse with modification. All images were cropped to 500 x 300 pixels with a white background.…”

“…Specifically, the present manipulation concerned not the task stimuli themselves but feedback events, and we were not interested in conflict-related memory, but employed incidental memory for feedback as an indicator of attention to reinforcement events. Thus, we made no assumptions about conflict effects on memory; we only assume that (a) reinforcement events are encoded in memory (e.g., Braun et al, 2018;Davidow et al, 2016;Gerraty et al, 2018;Höltje & Mecklinger, 2018, 2020 and that (b) subsequent memory is indicative of attentional states during encoding (e.g., Aly & Turk-Browne, 2016;Bejjani & Egner, 2019;Braun et al, 2018;Chiu & Egner, 2015;deBettencourt et al, 2017;Kim et al, 2018). Finally, the literature investigating conflict-related memory effects has typically employed tasks with 50% congruent and incongruent stimuli (but see Davis et al, 2019), where learning of contextual changes in control demand is, by definition, not possible.…”

“…Thus, one particularly important component of control-learning, and its interaction with sensitivity to statistical learning of contexts, may be how people exploit the (causal) structure of their environments to adjust their attention in a goal-conducive manner (cf. Bejjani & Egner, 2019). Structure learning refers to our ability to discriminate the causal relationship between variables in our environment.…”